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Surv."],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:p>For more than a century, the methods for data representation and the exploration of the intrinsic structures of data have developed remarkably and consist of supervised and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high and the data can come in messy, incomplete, unlabeled, or corrupted forms. Consequently, discovering the hidden structure buried inside such data becomes highly challenging. From this perspective, exploratory data analysis plays a substantial role in learning the hidden structures that encompass the significant features of the data in an ordered manner by extracting patterns and testing hypotheses to identify anomalies. Unsupervised generative learning models are a class of machine learning models characterized by their potential to reduce the dimensionality, discover the exploratory factors, and learn representations without any predefined labels; moreover, such models can generate the data from the reduced factors\u2019 domain. The beginner researchers can find in this survey the recent unsupervised generative learning models for the purpose of data exploration and learning representations; specifically, this article covers three families of methods based on their usage in the era of big data: blind source separation, manifold learning, and neural networks, from shallow to deep architectures.<\/jats:p>","DOI":"10.1145\/3450963","type":"journal-article","created":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T10:14:21Z","timestamp":1625825661000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":89,"title":["A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation Learning"],"prefix":"10.1145","volume":"54","author":[{"given":"Mohanad","family":"Abukmeil","sequence":"first","affiliation":[{"name":"Universit\u00e0 degli Studi di Milano, Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Ferrari","sequence":"additional","affiliation":[{"name":"Universit\u00e0 degli Studi di Milano, Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3683-4723","authenticated-orcid":false,"given":"Angelo","family":"Genovese","sequence":"additional","affiliation":[{"name":"Universit\u00e0 degli Studi di Milano, Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincenzo","family":"Piuri","sequence":"additional","affiliation":[{"name":"Universit\u00e0 degli Studi di Milano, Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Scotti","sequence":"additional","affiliation":[{"name":"Universit\u00e0 degli Studi di Milano, Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,7,9]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Proc. of CIVEMSA.","author":"Abukmeil M. 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In Proc. of ESANN. 199\u2013204."},{"key":"e_1_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1031596100"},{"key":"e_1_2_2_56_1","volume-title":"Proc. of NIPS. 658\u2013666","author":"Dosovitskiy Alexey","year":"2016","unstructured":"Alexey Dosovitskiy and Thomas Brox . 2016 . Generating images with perceptual similarity metrics based on deep networks . In Proc. of NIPS. 658\u2013666 . Alexey Dosovitskiy and Thomas Brox. 2016. Generating images with perceptual similarity metrics based on deep networks. In Proc. of NIPS. 658\u2013666."},{"key":"e_1_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11258-014-0406-z"},{"key":"e_1_2_2_58_1","volume-title":"Proc. of ICLR.","author":"Durugkar Ishan P.","year":"2017","unstructured":"Ishan P. Durugkar , Ian Gemp , and Sridhar Mahadevan . 2017 . Generative multi-adversarial networks . In Proc. of ICLR. Ishan P. Durugkar, Ian Gemp, and Sridhar Mahadevan. 2017. Generative multi-adversarial networks. 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Understanding the difficulty of training deep feedforward neural networks . In Proc. of AISTATS. 249\u2013256 . Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proc. of AISTATS. 249\u2013256."},{"key":"e_1_2_2_68_1","volume-title":"Proc. of NIPS. 513\u2013520","author":"Goldberger Jacob","unstructured":"Jacob Goldberger , Geoffrey E. Hinton , Sam T. Roweis , and Ruslan R. Salakhutdinov . 2005. Neighbourhood components analysis . In Proc. of NIPS. 513\u2013520 . Jacob Goldberger, Geoffrey E. Hinton, Sam T. Roweis, and Ruslan R. Salakhutdinov. 2005. Neighbourhood components analysis. In Proc. of NIPS. 513\u2013520."},{"key":"e_1_2_2_69_1","volume-title":"Proc. of NIPS. 548\u2013556","author":"Goodfellow Ian","year":"2013","unstructured":"Ian Goodfellow , Mehdi Mirza , Aaron Courville , and Yoshua Bengio . 2013 . Multi-prediction deep Boltzmann machines . In Proc. of NIPS. 548\u2013556 . 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Hierarchical topic models and the nested Chinese restaurant process . In Proc. of NIPS. 17\u201324 . Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum, and David M. Blei. 2004. Hierarchical topic models and the nested Chinese restaurant process. In Proc. of NIPS. 17\u201324."},{"key":"e_1_2_2_73_1","volume-title":"Proc. of NIPS. 5767\u20135777","author":"Gulrajani Ishaan","unstructured":"Ishaan Gulrajani , Faruk Ahmed , Martin Arjovsky , Vincent Dumoulin , and Aaron C. Courville . 2017. Improved training of Wasserstein GANs . In Proc. of NIPS. 5767\u20135777 . Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C. Courville. 2017. Improved training of Wasserstein GANs. 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Lagging inference networks and posterior collapse in variational autoencoders. arXiv:1901.05534  Junxian He Daniel Spokoyny Graham Neubig and Taylor Berg-Kirkpatrick. 2019. Lagging inference networks and posterior collapse in variational autoencoders. arXiv:1901.05534"},{"key":"e_1_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_2_78_1","volume-title":"Proc. of ICLR.","author":"Higgins Irina","year":"2017","unstructured":"Irina Higgins , Lo\u00efc Matthey , Arka Pal , Christopher Burgess , Xavier Glorot , Matthew Botvinick , Shakir Mohamed , and Alexander Lerchner . 2017 . beta-VAE: Learning basic visual concepts with a constrained variational framework . In Proc. of ICLR. Irina Higgins, Lo\u00efc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017. beta-VAE: Learning basic visual concepts with a constrained variational framework. In Proc. of ICLR."},{"key":"e_1_2_2_79_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"e_1_2_2_80_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976602760128018"},{"key":"e_1_2_2_81_1","volume-title":"Neural Networks: Tricks of the Trade.","author":"Hinton Geoffrey E.","unstructured":"Geoffrey E. Hinton . 2012. A practical guide to training restricted Boltzmann machines . In Neural Networks: Tricks of the Trade. Vol. 7700 . Springer , 599\u2013619. Geoffrey E. Hinton. 2012. A practical guide to training restricted Boltzmann machines. In Neural Networks: Tricks of the Trade. Vol. 7700. Springer, 599\u2013619."},{"key":"e_1_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"e_1_2_2_83_1","volume-title":"Proc. of NIPS. 857\u2013864","author":"Geoffrey","unstructured":"Geoffrey E. Hinton and Sam T. Roweis. 2002. Stochastic neighbor embedding . In Proc. of NIPS. 857\u2013864 . Geoffrey E. Hinton and Sam T. Roweis. 2002. 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MIT Press, Cambridge, MA."},{"key":"e_1_2_2_86_1","volume-title":"Proc. of NIPS. 3\u201310","author":"Geoffrey","unstructured":"Geoffrey E. Hinton and Richard S. Zemel. 1994. Autoencoders, minimum description length and Helmholtz free energy . In Proc. of NIPS. 3\u201310 . Geoffrey E. Hinton and Richard S. Zemel. 1994. Autoencoders, minimum description length and Helmholtz free energy. In Proc. of NIPS. 3\u201310."},{"key":"e_1_2_2_87_1","volume-title":"Proc. of TANC. 297\u2013306","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997 . Low-complexity coding and decoding . In Proc. of TANC. 297\u2013306 . Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Low-complexity coding and decoding. In Proc. of TANC. 297\u2013306."},{"key":"e_1_2_2_88_1","volume-title":"Proc. of ALPIT. 155\u2013158","author":"Hong He","year":"2007","unstructured":"He Hong . 2007 . Multimodal discovering and fusion for sSemantics multimedia analysis . In Proc. of ALPIT. 155\u2013158 . He Hong. 2007. Multimodal discovering and fusion for sSemantics multimedia analysis. In Proc. of ALPIT. 155\u2013158."},{"key":"e_1_2_2_89_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3301282","article-title":"How generative adversarial networks and their variants work: An overview","volume":"52","author":"Hong Yongjun","year":"2019","unstructured":"Yongjun Hong , Uiwon Hwang , Jaeyoon Yoo , and Sungroh Yoon . 2019 . How generative adversarial networks and their variants work: An overview . ACM Computing Surveys 52 , 1 (2019), 1 \u2013 43 . Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, and Sungroh Yoon. 2019. How generative adversarial networks and their variants work: An overview. ACM Computing Surveys 52, 1 (2019), 1\u201343.","journal-title":"ACM Computing Surveys"},{"key":"e_1_2_2_90_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2715285"},{"key":"e_1_2_2_91_1","volume-title":"Proc. of AAAI.","author":"Huang Lei","year":"2018","unstructured":"Lei Huang , Xianglong Liu , Bo Lang , Adams Yu , Yongliang Wang , and Bo Li . 2018 . Orthogonal weight normalization: Solution to optimization over multiple dependent Stiefel manifolds in deep neural networks . In Proc. of AAAI. Lei Huang, Xianglong Liu, Bo Lang, Adams Yu, Yongliang Wang, and Bo Li. 2018. Orthogonal weight normalization: Solution to optimization over multiple dependent Stiefel manifolds in deep neural networks. In Proc. of AAAI."},{"key":"e_1_2_2_92_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.202"},{"key":"e_1_2_2_93_1","volume-title":"Independent Component Analysis","author":"Hyv\u00e4rinen Aapo","unstructured":"Aapo Hyv\u00e4rinen , Juha Karhunen , and Erkki Oja . 2004. Independent Component Analysis . Vol. 46 . John Wiley & Sons . Aapo Hyv\u00e4rinen, Juha Karhunen, and Erkki Oja. 2004. Independent Component Analysis. Vol. 46. John Wiley & Sons."},{"key":"e_1_2_2_94_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(00)00026-5"},{"key":"e_1_2_2_95_1","volume-title":"Proc. of AAAI.","author":"Im Daniel Jiwoong","year":"2017","unstructured":"Daniel Jiwoong Im , Sungjin Ahn , Roland Memisevic , and Yoshua Bengio . 2017 . Denoising criterion for variational auto-encoding framework . In Proc. of AAAI. Daniel Jiwoong Im, Sungjin Ahn, Roland Memisevic, and Yoshua Bengio. 2017. Denoising criterion for variational auto-encoding framework. In Proc. of AAAI."},{"key":"e_1_2_2_96_1","volume-title":"Proc. of CVPR. 1125\u20131134","author":"Isola Phillip","unstructured":"Phillip Isola , Jun-Yan Zhu , Tinghui Zhou , and Alexei A. Efros . 2017. Image-to-image translation with conditional adversarial networks . In Proc. of CVPR. 1125\u20131134 . Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2017. Image-to-image translation with conditional adversarial networks. In Proc. of CVPR. 1125\u20131134."},{"key":"e_1_2_2_97_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511790423"},{"key":"e_1_2_2_98_1","volume-title":"Principal Component Analysis","author":"Jolliffe Ian","unstructured":"Ian Jolliffe . 2011. Principal Component Analysis . Springer . Ian Jolliffe. 2011. Principal Component Analysis. Springer."},{"key":"e_1_2_2_99_1","volume-title":"Riemannian Geometry and Geometric Analysis","author":"Jost J\u00fcrgen","unstructured":"J\u00fcrgen Jost and J\u00e8urgen Jost . 2008. Riemannian Geometry and Geometric Analysis . Vol. 42005 . Springer Science & Business Media . J\u00fcrgen Jost and J\u00e8urgen Jost. 2008. Riemannian Geometry and Geometric Analysis. Vol. 42005. Springer Science & Business Media."},{"key":"e_1_2_2_100_1","volume-title":"Kingma and Max Welling","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Max Welling . 2014 . Auto-encoding variational Bayes. In Proc. of ICLR. Diederik P. Kingma and Max Welling. 2014. Auto-encoding variational Bayes. In Proc. of ICLR."},{"key":"e_1_2_2_101_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1980.1102314"},{"key":"e_1_2_2_102_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.58325"},{"key":"e_1_2_2_103_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2012.09.018"},{"key":"e_1_2_2_104_1","doi-asserted-by":"publisher","DOI":"10.1137\/07070111X"},{"key":"e_1_2_2_105_1","volume-title":"Probabilistic Graphical Models: Principles and Techniques","author":"Koller Daphne","unstructured":"Daphne Koller , Nir Friedman , and Francis Bach . 2009. Probabilistic Graphical Models: Principles and Techniques . MIT Press, Cambridge , MA. Daphne Koller, Nir Friedman, and Francis Bach. 2009. Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge, MA."},{"key":"e_1_2_2_106_1","volume-title":"Proc. of NIPS. 1097\u20131105","author":"Krizhevsky Alex","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E. Hinton . 2012. ImageNet classification with deep convolutional neural networks . In Proc. of NIPS. 1097\u20131105 . Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proc. of NIPS. 1097\u20131105."},{"key":"e_1_2_2_107_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289694"},{"key":"e_1_2_2_108_1","volume-title":"Information Theory and Statistics","author":"Kullback Solomon","unstructured":"Solomon Kullback . 1997. Information Theory and Statistics . Courier Corporation . Solomon Kullback. 1997. Information Theory and Statistics. Courier Corporation."},{"key":"e_1_2_2_109_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2014.01.008"},{"key":"e_1_2_2_110_1","article-title":"Exploring strategies for training deep neural networks","author":"Larochelle Hugo","year":"2009","unstructured":"Hugo Larochelle , Yoshua Bengio , J\u00e9r\u00f4me Louradour , and Pascal Lamblin . 2009 . Exploring strategies for training deep neural networks . Journal of Machine Learning Research 10 ( Jan. 2009), 1\u201340. Hugo Larochelle, Yoshua Bengio, J\u00e9r\u00f4me Louradour, and Pascal Lamblin. 2009. Exploring strategies for training deep neural networks. Journal of Machine Learning Research 10 (Jan. 2009), 1\u201340.","journal-title":"Journal of Machine Learning Research 10"},{"key":"e_1_2_2_111_1","volume-title":"Proc. of ICML. 1558\u20131566","author":"Lindbo Larsen Anders Boesen","year":"2016","unstructured":"Anders Boesen Lindbo Larsen , S\u00f8ren Kaae S\u00f8nderby , Hugo Larochelle , and Ole Winther . 2016 . Autoencoding beyond pixels using a learned similarity metric . In Proc. of ICML. 1558\u20131566 . Anders Boesen Lindbo Larsen, S\u00f8ren Kaae S\u00f8nderby, Hugo Larochelle, and Ole Winther. 2016. Autoencoding beyond pixels using a learned similarity metric. In Proc. of ICML. 1558\u20131566."},{"key":"e_1_2_2_112_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.56"},{"key":"e_1_2_2_113_1","volume-title":"Learning the parts of objects by non-negative matrix factorization. Nature 401, 6755","author":"Lee Daniel D.","year":"1999","unstructured":"Daniel D. Lee and H. Sebastian Seung . 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401, 6755 ( 1999 ), 788\u2013791. Daniel D. Lee and H. Sebastian Seung. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401, 6755 (1999), 788\u2013791."},{"key":"e_1_2_2_114_1","volume-title":"Proc. of NIPS. 556\u2013562","author":"Lee Daniel D.","unstructured":"Daniel D. Lee and H. Sebastian Seung . 2001. Algorithms for non-negative matrix factorization . In Proc. of NIPS. 556\u2013562 . Daniel D. Lee and H. Sebastian Seung. 2001. Algorithms for non-negative matrix factorization. In Proc. of NIPS. 556\u2013562."},{"key":"e_1_2_2_115_1","volume-title":"Proc. of ICML. 609\u2013616","author":"Lee Honglak","unstructured":"Honglak Lee , Roger Grosse , Rajesh Ranganath , and Andrew Y. Ng . 2009. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations . In Proc. of ICML. 609\u2013616 . Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng. 2009. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proc. of ICML. 609\u2013616."},{"key":"e_1_2_2_116_1","doi-asserted-by":"publisher","DOI":"10.1145\/2996357"},{"key":"e_1_2_2_117_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143917"},{"key":"e_1_2_2_118_1","volume-title":"Proc. of NIPS. 1123\u20131131","author":"Li Wu-Jun","year":"2009","unstructured":"Wu-Jun Li , Dit-Yan Yeung , and Zhihua Zhang . 2009 . Probabilistic relational PCA . In Proc. of NIPS. 1123\u20131131 . Wu-Jun Li, Dit-Yan Yeung, and Zhihua Zhang. 2009. Probabilistic relational PCA. In Proc. of NIPS. 1123\u20131131."},{"key":"e_1_2_2_119_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2007.911536"},{"key":"e_1_2_2_120_1","volume-title":"Proc. of AISTATS. 635\u2013643","author":"Li Xin","year":"2015","unstructured":"Xin Li , Feipeng Zhao , and Yuhong Guo . 2015 . Conditional restricted Boltzmann machines for multi-label learning with incomplete labels . In Proc. of AISTATS. 635\u2013643 . Xin Li, Feipeng Zhao, and Yuhong Guo. 2015. Conditional restricted Boltzmann machines for multi-label learning with incomplete labels. In Proc. of AISTATS. 635\u2013643."},{"key":"e_1_2_2_121_1","volume-title":"Proc. of NIPS. 3155\u20133165","author":"Lin Kevin","year":"2017","unstructured":"Kevin Lin , Dianqi Li , Xiaodong He , Zhengyou Zhang , and Ming-Ting Sun . 2017 . Adversarial ranking for language generation . In Proc. of NIPS. 3155\u20133165 . Kevin Lin, Dianqi Li, Xiaodong He, Zhengyou Zhang, and Ming-Ting Sun. 2017. Adversarial ranking for language generation. In Proc. of NIPS. 3155\u20133165."},{"key":"e_1_2_2_122_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2008.06.025"},{"key":"e_1_2_2_123_1","doi-asserted-by":"publisher","DOI":"10.1109\/IHMSC.2016.101"},{"key":"e_1_2_2_124_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944979"},{"key":"e_1_2_2_125_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2060208"},{"key":"e_1_2_2_126_1","volume-title":"Proc. of AISTATS. 384\u2013391","author":"Maaten Laurens","year":"2009","unstructured":"Laurens Maaten . 2009 . Learning a parametric embedding by preserving local structure . In Proc. of AISTATS. 384\u2013391 . Laurens Maaten. 2009. Learning a parametric embedding by preserving local structure. In Proc. of AISTATS. 384\u2013391."},{"key":"e_1_2_2_127_1","unstructured":"Alireza Makhzani Jonathon Shlens Navdeep Jaitly Ian Goodfellow and Brendan Frey. 2015. Adversarial autoencoders. arXiv:1511.0564  Alireza Makhzani Jonathon Shlens Navdeep Jaitly Ian Goodfellow and Brendan Frey. 2015. Adversarial autoencoders. arXiv:1511.0564"},{"key":"e_1_2_2_128_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"e_1_2_2_129_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21735-7_7"},{"key":"e_1_2_2_130_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.383036"},{"key":"e_1_2_2_131_1","unstructured":"Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv:1411.1784  Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv:1411.1784"},{"key":"e_1_2_2_132_1","volume-title":"Proc. of UAI. 514\u2013522","author":"Mnih Volodymyr","unstructured":"Volodymyr Mnih , Hugo Larochelle , and Geoffrey E. Hinton . 2011. Conditional restricted Boltzmann machines for structured output prediction . In Proc. of UAI. 514\u2013522 . Volodymyr Mnih, Hugo Larochelle, and Geoffrey E. Hinton. 2011. Conditional restricted Boltzmann machines for structured output prediction. In Proc. of UAI. 514\u2013522."},{"key":"e_1_2_2_133_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2011.2109382"},{"key":"e_1_2_2_134_1","first-page":"1","article-title":"Latent variable mixture modeling","volume":"2","author":"Muth\u00e9n Bengt","year":"2001","unstructured":"Bengt Muth\u00e9n . 2001 . Latent variable mixture modeling . New Developments and Techniques in Structural Equation Modeling 2 (2001), 1 \u2013 33 . Bengt Muth\u00e9n. 2001. Latent variable mixture modeling. New Developments and Techniques in Structural Equation Modeling 2 (2001), 1\u201333.","journal-title":"New Developments and Techniques in Structural Equation Modeling"},{"key":"e_1_2_2_135_1","volume-title":"Latent variable analysis","author":"Muth\u00e9n Bengt","unstructured":"Bengt Muth\u00e9n . 2004. Latent variable analysis . In The Sage Handbook of Quantitative Methodology for the Social Sciences, D. Kaplan (Ed.). Sage, Newbury Park, CA , 106\u2013109. Bengt Muth\u00e9n. 2004. Latent variable analysis. In The Sage Handbook of Quantitative Methodology for the Social Sciences, D. Kaplan (Ed.). Sage, Newbury Park, CA, 106\u2013109."},{"key":"e_1_2_2_136_1","volume-title":"NETLAB: Algorithms for Pattern Recognition","author":"Nabney Ian","year":"2002","unstructured":"Ian Nabney . 2002 . NETLAB: Algorithms for Pattern Recognition . Springer Science & Business Media . Ian Nabney. 2002. NETLAB: Algorithms for Pattern Recognition. Springer Science & Business Media."},{"key":"e_1_2_2_137_1","volume-title":"Sparse autoencoder. CS294A Lecture Notes 72","author":"Andrew Ng.","year":"2011","unstructured":"Andrew Ng. 2011. Sparse autoencoder. CS294A Lecture Notes 72 , 2011 (2011), 1\u201319. Andrew Ng. 2011. Sparse autoencoder. CS294A Lecture Notes 72, 2011 (2011), 1\u201319."},{"key":"e_1_2_2_138_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.374"},{"key":"e_1_2_2_139_1","volume-title":"Proc. of NIPS. 3387\u20133395","author":"Nguyen Anh","year":"2016","unstructured":"Anh Nguyen , Alexey Dosovitskiy , Jason Yosinski , Thomas Brox , and Jeff Clune . 2016 . Synthesizing the preferred inputs for neurons in neural networks via deep generator networks . In Proc. of NIPS. 3387\u20133395 . Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune. 2016. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In Proc. of NIPS. 3387\u20133395."},{"key":"e_1_2_2_140_1","volume-title":"Proc. of NIPS. 1106\u20131114","author":"Nguyen Viet-An","year":"2013","unstructured":"Viet-An Nguyen , Jordan L. Ying , and Philip Resnik . 2013 . Lexical and hierarchical topic regression . In Proc. of NIPS. 1106\u20131114 . Viet-An Nguyen, Jordan L. Ying, and Philip Resnik. 2013. Lexical and hierarchical topic regression. In Proc. of NIPS. 1106\u20131114."},{"key":"e_1_2_2_141_1","doi-asserted-by":"publisher","DOI":"10.1002\/env.3170050203"},{"key":"e_1_2_2_142_1","doi-asserted-by":"publisher","DOI":"10.1137\/0718026"},{"key":"e_1_2_2_143_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00244"},{"key":"e_1_2_2_144_1","doi-asserted-by":"publisher","DOI":"10.1080\/14786440109462720"},{"key":"e_1_2_2_145_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008981510081"},{"key":"e_1_2_2_146_1","volume-title":"Proc. of HLT. 670\u2013675","author":"Petinot Yves","year":"2011","unstructured":"Yves Petinot , Kathleen McKeown , and Kapil Thadani . 2011 . A hierarchical model of web summaries . In Proc. of HLT. 670\u2013675 . Yves Petinot, Kathleen McKeown, and Kapil Thadani. 2011. A hierarchical model of web summaries. In Proc. of HLT. 670\u2013675."},{"key":"e_1_2_2_147_1","first-page":"92","article-title":"A survey on deep learning: Algorithms, techniques, and applications","volume":"51","author":"Pouyanfar Samira","year":"2018","unstructured":"Samira Pouyanfar , Saad Sadiq , Yilin Yan , Haiman Tian , Yudong Tao , Maria Presa Reyes , Mei-Ling Shyu , Shu-Ching Chen , and S. S. Iyengar . 2018 . A survey on deep learning: Algorithms, techniques, and applications . ACM Computing Surveys 51 , 5 (2018), 92 . Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and S. S. Iyengar. 2018. A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys 51, 5 (2018), 92.","journal-title":"ACM Computing Surveys"},{"key":"e_1_2_2_148_1","doi-asserted-by":"publisher","DOI":"10.1145\/3150226"},{"key":"e_1_2_2_149_1","unstructured":"Alec Radford Luke Metz and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434  Alec Radford Luke Metz and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434"},{"key":"e_1_2_2_150_1","volume-title":"Encyclopedia of Biometrics, Stan Z. Li and Anil Jain (Eds)","author":"Reynolds Douglas A.","unstructured":"Douglas A. Reynolds . 2009. Gaussian mixture models . In Encyclopedia of Biometrics, Stan Z. Li and Anil Jain (Eds) . Springer , 741. Douglas A. Reynolds. 2009. Gaussian mixture models. In Encyclopedia of Biometrics, Stan Z. Li and Anil Jain (Eds). Springer, 741."},{"key":"e_1_2_2_151_1","volume-title":"Proc. of ICML.","author":"Rezende Danilo Jimenez","year":"2014","unstructured":"Danilo Jimenez Rezende , Shakir Mohamed , and Daan Wierstra . 2014 . Stochastic backpropagation and approximate inference in deep generative models . In Proc. of ICML. Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In Proc. of ICML."},{"key":"e_1_2_2_152_1","volume-title":"Proc. of ICML. 833\u2013840","author":"Rifai Salah","year":"2011","unstructured":"Salah Rifai , Pascal Vincent , Xavier Muller , Xavier Glorot , and Yoshua Bengio . 2011 . Contractive auto-encoders: Explicit invariance during feature extraction . In Proc. of ICML. 833\u2013840 . Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio. 2011. Contractive auto-encoders: Explicit invariance during feature extraction. In Proc. of ICML. 833\u2013840."},{"key":"e_1_2_2_153_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_2_154_1","unstructured":"Mihaela Rosca Balaji Lakshminarayanan David Warde-Farley and Shakir Mohamed. 2017. Variational approaches for auto-encoding generative adversarial networks. arXiv:1706.04987  Mihaela Rosca Balaji Lakshminarayanan David Warde-Farley and Shakir Mohamed. 2017. Variational approaches for auto-encoding generative adversarial networks. arXiv:1706.04987"},{"key":"e_1_2_2_155_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976699300016674"},{"key":"e_1_2_2_156_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"e_1_2_2_157_1","volume-title":"Williams","author":"Rumelhart David E.","year":"1985","unstructured":"David E. Rumelhart , Geoffrey E. Hinton , and Ronald J . Williams . 1985 . Learning Internal Representations by Error Propagation. Technical Report. La Jolla Institute for Cognitive Science, University of California , San Diego. David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1985. Learning Internal Representations by Error Propagation. Technical Report. La Jolla Institute for Cognitive Science, University of California, San Diego."},{"key":"e_1_2_2_158_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-010814-020120"},{"key":"e_1_2_2_159_1","volume-title":"Proc. of AISTATS. 448\u2013455","author":"Salakhutdinov Ruslan","year":"2009","unstructured":"Ruslan Salakhutdinov and Geoffrey Hinton . 2009 . Deep Boltzmann machines . In Proc. of AISTATS. 448\u2013455 . Ruslan Salakhutdinov and Geoffrey Hinton. 2009. Deep Boltzmann machines. In Proc. of AISTATS. 448\u2013455."},{"key":"e_1_2_2_160_1","volume-title":"Proc. of NIPS. 2234\u20132242","author":"Salimans Tim","year":"2016","unstructured":"Tim Salimans , Ian Goodfellow , Wojciech Zaremba , Vicki Cheung , Alec Radford , and Xi Chen . 2016 . Improved techniques for training GANs . In Proc. of NIPS. 2234\u20132242 . Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training GANs. In Proc. of NIPS. 2234\u20132242."},{"key":"e_1_2_2_161_1","article-title":"Think globally, fit locally: Unsupervised learning of low dimensional manifolds","author":"Saul Lawrence K.","year":"2003","unstructured":"Lawrence K. Saul and Sam T. Roweis . 2003 . Think globally, fit locally: Unsupervised learning of low dimensional manifolds . Journal of Machine Learning Research 4 ( June 2003), 119\u2013155. Lawrence K. Saul and Sam T. Roweis. 2003. Think globally, fit locally: Unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research 4 (June 2003), 119\u2013155.","journal-title":"Journal of Machine Learning Research 4"},{"key":"e_1_2_2_162_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_2_2_163_1","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0020217"},{"key":"e_1_2_2_164_1","volume-title":"Proc. of AISTATS. 460\u2013467","author":"Shaw Blake","year":"2007","unstructured":"Blake Shaw and Tony Jebara . 2007 . Minimum volume embedding . In Proc. of AISTATS. 460\u2013467 . Blake Shaw and Tony Jebara. 2007. Minimum volume embedding. In Proc. of AISTATS. 460\u2013467."},{"key":"e_1_2_2_165_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553494"},{"key":"e_1_2_2_166_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.868688"},{"key":"e_1_2_2_167_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2009.29"},{"key":"e_1_2_2_168_1","volume-title":"Proc. of NIPS. 721\u2013728","author":"Vin","unstructured":"Vin D. Silva and Joshua B. Tenenbaum. 2003. Global versus local methods in nonlinear dimensionality reduction . In Proc. of NIPS. 721\u2013728 . Vin D. Silva and Joshua B. Tenenbaum. 2003. Global versus local methods in nonlinear dimensionality reduction. In Proc. of NIPS. 721\u2013728."},{"key":"e_1_2_2_169_1","volume-title":"Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations, David E","author":"Smolensky P.","unstructured":"P. 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Ng. 2011. Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In Proc. of NIPS. 801\u2013809."},{"key":"e_1_2_2_171_1","volume-title":"Proc. of EMNLP. 151\u2013161","author":"Socher Richard","unstructured":"Richard Socher , Jeffrey Pennington , Eric H. Huang , Andrew Y. Ng , and Christopher D. Manning . 2011. Semi-supervised recursive autoencoders for predicting sentiment distributions . In Proc. of EMNLP. 151\u2013161 . Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. 2011. Semi-supervised recursive autoencoders for predicting sentiment distributions. In Proc. of EMNLP. 151\u2013161."},{"key":"e_1_2_2_172_1","volume-title":"Proc. of ICCV. 648\u2013653","author":"Souvenir R.","unstructured":"R. Souvenir and R. Pless . 2005. Manifold clustering . In Proc. of ICCV. 648\u2013653 . R. Souvenir and R. Pless. 2005. Manifold clustering. In Proc. of ICCV. 648\u2013653."},{"key":"e_1_2_2_173_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_2_2_174_1","volume-title":"Proc. of NIPS. 2222\u20132230","author":"Srivastava Nitish","unstructured":"Nitish Srivastava and Ruslan R. Salakhutdinov . 2012. Multimodal learning with deep Boltzmann machines . In Proc. of NIPS. 2222\u20132230 . Nitish Srivastava and Ruslan R. Salakhutdinov. 2012. Multimodal learning with deep Boltzmann machines. In Proc. of NIPS. 2222\u20132230."},{"key":"e_1_2_2_175_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0036144504443821"},{"key":"e_1_2_2_176_1","volume-title":"Proc. of NIPS. 865\u2013872","author":"Yee","unstructured":"Yee W. Teh and Sam T. Roweis. 2003. Automatic alignment of local representations . In Proc. of NIPS. 865\u2013872 . Yee W. Teh and Sam T. Roweis. 2003. Automatic alignment of local representations. In Proc. of NIPS. 865\u2013872."},{"key":"e_1_2_2_177_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2319"},{"key":"e_1_2_2_178_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00196"},{"key":"e_1_2_2_179_1","volume-title":"Contributions to Mathematical Psychology","author":"Ledyard R.","unstructured":"Ledyard R. Tucker 1964. The extension of factor analysis to three-dimensional matrices . In Contributions to Mathematical Psychology , N. Frederiksen and H. Gulliksen (Eds.). Holt, Rinehart & Winston, New York, NY , 109\u2013127. Ledyard R. Tucker 1964. The extension of factor analysis to three-dimensional matrices. In Contributions to Mathematical Psychology, N. Frederiksen and H. Gulliksen (Eds.). Holt, Rinehart & Winston, New York, NY, 109\u2013127."},{"key":"e_1_2_2_180_1","first-page":"3221","article-title":"Accelerating t-SNE using tree-based algorithms","volume":"15","author":"der Maaten Laurens Van","year":"2014","unstructured":"Laurens Van der Maaten . 2014 . Accelerating t-SNE using tree-based algorithms . Journal of Machine Learning Research 15 , 1 (2014), 3221 \u2013 3245 . Laurens Van der Maaten. 2014. Accelerating t-SNE using tree-based algorithms. Journal of Machine Learning Research 15, 1 (2014), 3221\u20133245.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_2_181_1","article-title":"Visualizing data using t-SNE","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of Machine Learning Research 9 ( Nov. 2008), 2579\u20132605. Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9 (Nov. 2008), 2579\u20132605.","journal-title":"Journal of Machine Learning Research 9"},{"key":"e_1_2_2_182_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5273-4"},{"key":"e_1_2_2_183_1","first-page":"2009","article-title":"Dimensionality Reduction","author":"der Maaten Laurens Van","year":"2009","unstructured":"Laurens Van der Maaten , Eric Postma , and Jaap Van den Herik . 2009 . Dimensionality Reduction : A Comparative Review. Technical Report TiCC-TR 2009 - 2005 . Tilburg University. Laurens Van der Maaten, Eric Postma, and Jaap Van den Herik. 2009. Dimensionality Reduction: A Comparative Review. Technical Report TiCC-TR 2009-005. Tilburg University.","journal-title":"A Comparative Review. Technical Report TiCC-TR"},{"key":"e_1_2_2_184_1","doi-asserted-by":"publisher","DOI":"10.1137\/1038003"},{"key":"e_1_2_2_185_1","volume-title":"Proc. of NIPS. 849\u2013856","author":"Vincent Pascal","year":"2003","unstructured":"Pascal Vincent and Yoshua Bengio . 2003 . Manifold Parzen windows . In Proc. of NIPS. 849\u2013856 . Pascal Vincent and Yoshua Bengio. 2003. Manifold Parzen windows. In Proc. of NIPS. 849\u2013856."},{"key":"e_1_2_2_186_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"e_1_2_2_187_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756006.1953039"},{"key":"e_1_2_2_188_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00917"},{"key":"e_1_2_2_189_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.51"},{"key":"e_1_2_2_190_1","unstructured":"Yanyan Wei Zhao Zhang Jicong Fan Yang Wang Shuicheng Yan and Meng Wang. 2019. DerainCycleGAN: An attention-guided unsupervised benchmark for single image deraining and rainmaking. arXiv:1912.07015  Yanyan Wei Zhao Zhang Jicong Fan Yang Wang Shuicheng Yan and Meng Wang. 2019. DerainCycleGAN: An attention-guided unsupervised benchmark for single image deraining and rainmaking. arXiv:1912.07015"},{"key":"e_1_2_2_191_1","doi-asserted-by":"crossref","unstructured":"Yanyan Wei Zhao Zhang Haijun Zhang Jie Qin and Mingbo Zhao. 2020. Semi-DerainGAN: A new semi-supervised single image deraining network. arXiv:2001.08388  Yanyan Wei Zhao Zhang Haijun Zhang Jie Qin and Mingbo Zhao. 2020. Semi-DerainGAN: A new semi-supervised single image deraining network. arXiv:2001.08388","DOI":"10.1109\/ICME51207.2021.9428285"},{"key":"e_1_2_2_192_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-005-4939-z"},{"key":"e_1_2_2_193_1","volume-title":"Saul","author":"Weinberger Kilian Q.","year":"2004","unstructured":"Kilian Q. Weinberger , Fei Sha , and Lawrence K . Saul . 2004 . Learning a kernel matrix for nonlinear dimensionality reduction. In Proc. of ICML. 106. Kilian Q. Weinberger, Fei Sha, and Lawrence K. Saul. 2004. Learning a kernel matrix for nonlinear dimensionality reduction. In Proc. of ICML. 106."},{"key":"e_1_2_2_194_1","volume-title":"Proc. of ICANN. 351\u2013357","author":"Welling Max","unstructured":"Max Welling and Geoffrey E. Hinton . 2002. A new learning algorithm for mean field Boltzmann machines . In Proc. of ICANN. 351\u2013357 . Max Welling and Geoffrey E. Hinton. 2002. A new learning algorithm for mean field Boltzmann machines. In Proc. of ICANN. 351\u2013357."},{"key":"e_1_2_2_195_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1012485807823"},{"key":"e_1_2_2_196_1","doi-asserted-by":"publisher","DOI":"10.1016\/0169-7439(87)80084-9"},{"key":"e_1_2_2_197_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2886012"},{"key":"e_1_2_2_198_1","volume-title":"Proc. of NIPS. 82\u201390","author":"Wu Jiajun","year":"2016","unstructured":"Jiajun Wu , Chengkai Zhang , Tianfan Xue , Bill Freeman , and Josh Tenenbaum . 2016 . Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling . In Proc. of NIPS. 82\u201390 . Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. 2016. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In Proc. of NIPS. 82\u201390."},{"key":"e_1_2_2_199_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126519"},{"key":"e_1_2_2_200_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2458702"},{"key":"e_1_2_2_201_1","doi-asserted-by":"publisher","DOI":"10.1186\/1755-8794-7-S2-S1"},{"key":"e_1_2_2_202_1","volume-title":"Proc. of ICIP.","author":"Yang Ming-Hsuan","year":"2002","unstructured":"Ming-Hsuan Yang . 2002 . Face recognition using extended Isomap . In Proc. of ICIP. Ming-Hsuan Yang. 2002. Face recognition using extended Isomap. In Proc. of ICIP."},{"key":"e_1_2_2_203_1","first-page":"131","article-title":"Exploratory data analysis","volume":"2","author":"Yu Chong Ho","year":"1977","unstructured":"Chong Ho Yu . 1977 . Exploratory data analysis . Methods 2 (1977), 131 \u2013 160 . Chong Ho Yu. 1977. Exploratory data analysis. Methods 2 (1977), 131\u2013160.","journal-title":"Methods"},{"key":"e_1_2_2_204_1","volume-title":"Proc. of ICML. 1215\u20131222","author":"Yu Kai","year":"2010","unstructured":"Kai Yu and Tong Zhang . 2010 . Improved local coordinate coding using local tangents . In Proc. of ICML. 1215\u20131222 . Kai Yu and Tong Zhang. 2010. Improved local coordinate coding using local tangents. In Proc. of ICML. 1215\u20131222."},{"key":"e_1_2_2_205_1","volume-title":"Proc. of NIPS. 2223\u20132231","author":"Yu Kai","year":"2009","unstructured":"Kai Yu , Tong Zhang , and Yihong Gong . 2009 . Nonlinear learning using local coordinate coding . In Proc. of NIPS. 2223\u20132231 . Kai Yu, Tong Zhang, and Yihong Gong. 2009. Nonlinear learning using local coordinate coding. In Proc. of NIPS. 2223\u20132231."},{"key":"e_1_2_2_206_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2889774"},{"key":"e_1_2_2_207_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-1378-9"},{"key":"e_1_2_2_208_1","volume-title":"Proc. of ICML. 7354\u20137363","author":"Zhang Han","year":"2019","unstructured":"Han Zhang , Ian Goodfellow , Dimitris Metaxas , and Augustus Odena . 2019 . Self-attention generative adversarial networks . In Proc. of ICML. 7354\u20137363 . Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. 2019. Self-attention generative adversarial networks. In Proc. of ICML. 7354\u20137363."},{"key":"e_1_2_2_209_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2877746"},{"key":"e_1_2_2_210_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.09.043"},{"key":"e_1_2_2_211_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976236.41"},{"key":"e_1_2_2_212_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2012.2202901"},{"key":"e_1_2_2_213_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2654163"},{"key":"e_1_2_2_214_1","doi-asserted-by":"publisher","DOI":"10.1137\/S1064827502419154"},{"key":"e_1_2_2_215_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2940576"},{"key":"e_1_2_2_216_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2893956"},{"key":"e_1_2_2_217_1","unstructured":"Zhao Zhang Yan Zhang Li Zhang and Shuicheng Yan. 2020. A survey on concept factorization: From shallow to deep representation learning. arXiv:2007.15840  Zhao Zhang Yan Zhang Li Zhang and Shuicheng Yan. 2020. 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