{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:26:02Z","timestamp":1740122762874,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,1,11]],"date-time":"2020-01-11T00:00:00Z","timestamp":1578700800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,11]],"date-time":"2020-01-11T00:00:00Z","timestamp":1578700800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s11063-020-10194-y","type":"journal-article","created":{"date-parts":[[2020,1,11]],"date-time":"2020-01-11T05:33:10Z","timestamp":1578720790000},"page":"1973-1988","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Image Clustering Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids and MMD Distance"],"prefix":"10.1007","volume":"51","author":[{"given":"Qiuyu","family":"Zhu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8076-9079","authenticated-orcid":false,"given":"Zhengyong","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,11]]},"reference":[{"key":"10194_CR1","unstructured":"Cai D, He X, Wang X, Bao H, Han J (2009) Locality preserving nonnegative matrix factorization. In: Twenty-first international joint conference on artificial intelligence"},{"key":"10194_CR2","doi-asserted-by":"crossref","unstructured":"Chang J, Wang L, Meng G, Xiang S, Pan C (2017) Deep adaptive image clustering. In: Proceedings of the IEEE international conference on computer vision, pp 5879\u20135887","DOI":"10.1109\/ICCV.2017.626"},{"key":"10194_CR3","unstructured":"Chen D, Lv J, Zhang Y (2017) Unsupervised multi-manifold clustering by learning deep representation. In: Workshops at the thirty-first AAAI conference on artificial intelligence"},{"key":"10194_CR4","doi-asserted-by":"crossref","unstructured":"Chen X, Cai D (2011) Large scale spectral clustering with landmark-based representation. In: Twenty-fifth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v25i1.7900"},{"issue":"1","key":"10194_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc: Ser B (Methodological) 39(1):1\u201322","journal-title":"J R Stat Soc: Ser B (Methodological)"},{"issue":"6","key":"10194_CR6","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng L (2012) The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Process Mag 29(6):141\u2013142","journal-title":"IEEE Signal Process Mag"},{"key":"10194_CR7","doi-asserted-by":"crossref","unstructured":"Ding C, He X (2004) K-means clustering via principal component analysis. In: Proceedings of the twenty-first international conference on machine learning, ICML \u201904. ACM, New York, p. 29","DOI":"10.1145\/1015330.1015408"},{"key":"10194_CR8","unstructured":"Doersch C (2016) Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908"},{"issue":"10","key":"10194_CR9","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1109\/34.954598","volume":"23","author":"Y Gdalyahu","year":"2001","unstructured":"Gdalyahu Y, Weinshall D, Werman M (2001) Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization. IEEE Trans Pattern Anal Mach Intell 23(10):1053\u20131074","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10194_CR10","unstructured":"Ghasedi\u00a0Dizaji K, Herandi A, Deng C, Cai W, Huang H (2017) Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization. In: Proceedings of the IEEE international conference on computer vision, pp 5736\u20135745"},{"issue":"2","key":"10194_CR11","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/0031-3203(78)90018-3","volume":"10","author":"KC Gowda","year":"1978","unstructured":"Gowda KC, Krishna G (1978) Agglomerative clustering using the concept of mutual nearest neighbourhood. Pattern Recognit 10(2):105\u2013112","journal-title":"Pattern Recognit"},{"key":"10194_CR12","doi-asserted-by":"crossref","unstructured":"Guo X, Gao L, Liu X, and Yin J (2017) Improved deep embedded clustering with local structure preservation. In: IJCAI, pp 1753\u20131759","DOI":"10.24963\/ijcai.2017\/243"},{"key":"10194_CR13","unstructured":"Guo X, Zhu E, Liu X, Yin J (2018) Deep embedded clustering with data augmentation. In: Asian conference on machine learning, pp 550\u2013565"},{"key":"10194_CR14","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"12","key":"10194_CR15","doi-asserted-by":"publisher","first-page":"5659","DOI":"10.1109\/TIP.2015.2487860","volume":"24","author":"C Hong","year":"2015","unstructured":"Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659\u20135670","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"10194_CR16","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1109\/TMM.2017.2745702","volume":"20","author":"C-C Hsu","year":"2017","unstructured":"Hsu C-C, Lin C-W (2017) Cnn-based joint clustering and representation learning with feature drift compensation for large-scale image data. IEEE Trans Multimedia 20(2):421\u2013429","journal-title":"IEEE Trans Multimedia"},{"key":"10194_CR17","unstructured":"Hu W, Miyato T, Tokui S, Matsumoto E, Sugiyama M (2017) Learning discrete representations via information maximizing self-augmented training. In: Proceedings of the 34th international conference on machine learning, vol. 70, pp 1558\u20131567. JMLR. org"},{"key":"10194_CR18","doi-asserted-by":"crossref","unstructured":"Huang P, Huang Y, Wang W, Wang L (2014) Deep embedding network for clustering. In: 2014 22nd International conference on pattern recognition, pp 1532\u20131537. IEEE","DOI":"10.1109\/ICPR.2014.272"},{"key":"10194_CR19","unstructured":"Jiang Z, Zheng Y, Tan H, Tang B, Zhou H (2016) Variational deep embedding: an unsupervised and generative approach to clustering. arXiv preprint arXiv:1611.05148"},{"key":"10194_CR20","unstructured":"Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114"},{"issue":"1","key":"10194_CR21","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79\u201386","journal-title":"Ann Math Stat"},{"issue":"3","key":"10194_CR22","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/0031-3203(91)90062-A","volume":"24","author":"T Kurita","year":"1991","unstructured":"Kurita T (1991) An efficient agglomerative clustering algorithm using a heap. Pattern Recognit 24(3):205\u2013209","journal-title":"Pattern Recognit"},{"key":"10194_CR23","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.patcog.2018.05.019","volume":"83","author":"F Li","year":"2018","unstructured":"Li F, Qiao H, Zhang B (2018) Discriminatively boosted image clustering with fully convolutional auto-encoders. Pattern Recognit 83:161\u2013173","journal-title":"Pattern Recognit"},{"key":"10194_CR24","doi-asserted-by":"crossref","unstructured":"Liu X, Dou Y, Yin J, Wang L, Zhu E (2016) Multiple kernel k-means clustering with matrix-induced regularization. In: Thirtieth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v30i1.10249"},{"key":"10194_CR25","unstructured":"MacQueen J et\u00a0al. (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol\u00a01. Oakland, CA, USA, pp 281\u2013297"},{"key":"10194_CR26","volume-title":"Finite mixture models","author":"G McLachlan","year":"2004","unstructured":"McLachlan G, Peel D (2004) Finite mixture models. Wiley, London"},{"key":"10194_CR27","unstructured":"Nene SA, Nayar SK, Murase H, et al (1996) Columbia object image library (coil-20)"},{"key":"10194_CR28","unstructured":"Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: analysis and an algorithm. In: Advances in neural information processing systems, pp 849\u2013856"},{"key":"10194_CR29","doi-asserted-by":"crossref","unstructured":"Peng X, Feng J, Lu J, Yau W-Y, Yi Z (2017) Cascade subspace clustering. In: Thirty-first AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.10824"},{"key":"10194_CR30","unstructured":"Saito S, Tan RT (2017) Neural clustering: concatenating layers for better projections"},{"issue":"37","key":"10194_CR31","doi-asserted-by":"publisher","first-page":"9814","DOI":"10.1073\/pnas.1700770114","volume":"114","author":"SA Shah","year":"2017","unstructured":"Shah SA, Koltun V (2017) Robust continuous clustering. Proc Natl Acad Sci 114(37):9814\u20139819","journal-title":"Proc Natl Acad Sci"},{"key":"10194_CR32","unstructured":"Shi J, Malik J (2000) Normalized cuts and image segmentation. Departmental Papers (CIS), p 107"},{"key":"10194_CR33","doi-asserted-by":"crossref","unstructured":"Song C, Liu F, Huang Y, Wang L, Tan T (2013) Auto-encoder based data clustering. In: Iberoamerican congress on pattern recognition. Springer, Berlin, pp 117\u2013124","DOI":"10.1007\/978-3-642-41822-8_15"},{"issue":"Dec","key":"10194_CR34","first-page":"583","volume":"3","author":"A Strehl","year":"2002","unstructured":"Strehl A, Ghosh J (2002) Cluster ensembles: a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3(Dec):583\u2013617","journal-title":"J Mach Learn Res"},{"key":"10194_CR35","unstructured":"Trigeorgis G, Bousmalis K, Zafeiriou S, Schuller BW. Supplementary material for a deep semi-NMF model for learning hidden representations"},{"issue":"Dec","key":"10194_CR36","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(Dec):3371\u20133408","journal-title":"J Mach Learn Res"},{"key":"10194_CR37","doi-asserted-by":"crossref","unstructured":"Wang Z, Chang S, Zhou J, Wang M, Huang TS (2016) Learning a task-specific deep architecture for clustering. In: Proceedings of the 2016 SIAM international conference on data mining. SIAM, pp 369\u2013377","DOI":"10.1137\/1.9781611974348.42"},{"key":"10194_CR38","unstructured":"Xiao H, Rasul K, Vollgraf R (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747"},{"key":"10194_CR39","unstructured":"Xie J, Girshick R, Farhadi A (2016) Unsupervised deep embedding for clustering analysis. In: International conference on machine learning, pp 478\u2013487"},{"key":"10194_CR40","unstructured":"Yang B, Fu X, Sidiropoulos ND, Hong M (2017) Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In: Proceedings of the 34th international conference on machine learning, vol 70, pp 3861\u20133870. JMLR. org"},{"key":"10194_CR41","doi-asserted-by":"crossref","unstructured":"Yang J, Parikh D, Batra D (2016) Joint unsupervised learning of deep representations and image clusters. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5147\u20135156","DOI":"10.1109\/CVPR.2016.556"},{"key":"10194_CR42","doi-asserted-by":"crossref","unstructured":"Zhang J, Li K, Liang Y, Li N (2017) Learning 3D faces from 2D images via stacked contractive autoencoder. Neurocomputing, S0925231217301431","DOI":"10.1016\/j.neucom.2016.11.062"},{"key":"10194_CR43","doi-asserted-by":"crossref","unstructured":"Zhang J, Yu J, Tao D (2018) Local deep-feature alignment for unsupervised dimension reduction. IEEE Trans Image Process, pp 1\u20131","DOI":"10.1109\/TIP.2018.2804218"},{"key":"10194_CR44","doi-asserted-by":"crossref","unstructured":"Zhang W, Wang X, Zhao D, Tang X (2012) Graph degree linkage: agglomerative clustering on a directed graph. In: European conference on computer vision. Springer, Berlin, pp 428\u2013441","DOI":"10.1007\/978-3-642-33718-5_31"},{"key":"10194_CR45","unstructured":"Zhao D, Tang X (2009) Cyclizing clusters via zeta function of a graph. In: Advances in neural information processing systems, pp 1953\u20131960"},{"key":"10194_CR46","unstructured":"Zhu Q, Zhang R (2019) A classification supervised auto-encoder based on predefined evenly-distributed class centroids. arXiv preprint arXiv:1902.00220"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10194-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-020-10194-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10194-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:21:51Z","timestamp":1722298911000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-020-10194-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,11]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["10194"],"URL":"https:\/\/doi.org\/10.1007\/s11063-020-10194-y","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2020,1,11]]},"assertion":[{"value":"11 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}