{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T14:17:45Z","timestamp":1779891465840,"version":"3.53.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2015,4,25]],"date-time":"2015-04-25T00:00:00Z","timestamp":1429920000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2016,5]]},"DOI":"10.1007\/s00521-015-1916-x","type":"journal-article","created":{"date-parts":[[2015,4,24]],"date-time":"2015-04-24T06:40:05Z","timestamp":1429857605000},"page":"1019-1031","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["Efficient and robust deep learning with Correntropy-induced loss function"],"prefix":"10.1007","volume":"27","author":[{"given":"Liangjun","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Badong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose C.","family":"Principe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2015,4,25]]},"reference":[{"issue":"7","key":"1916_CR1","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"G Hinton","year":"2006","unstructured":"Hinton G, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527\u20131554","journal-title":"Neural Comput"},{"key":"1916_CR2","doi-asserted-by":"crossref","unstructured":"Bengio Y et al (2007) Greedy layer-wise training of deep networks. In: Advances in neural information processing systems, vol 19 (NIPS06). MIT Press, pp 153\u2013160","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"1916_CR3","unstructured":"Poultney C, Chopra, S, Cun YL (2006) Efficient learning of sparse representations with an energy-based model. In: Advances in neural information processing systems, pp 1137\u20131144"},{"issue":"5786","key":"1916_CR4","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507","journal-title":"Science"},{"key":"1916_CR5","unstructured":"Freund Y, Haussler D (1992) Unsupervised learning of distributions on binary vectors using two layer networks. In: Advances in neural information processing systems 4. Morgan Kaufmann, San Mateo, CA, pp 912\u2013919"},{"key":"1916_CR6","doi-asserted-by":"crossref","unstructured":"Mobahi H, Collobert R, Weston J (2009) Deep learning from temporal coherence in video. In: Proceedings of the 26th annual international conference on machine learning. ACM","DOI":"10.1145\/1553374.1553469"},{"key":"1916_CR7","doi-asserted-by":"crossref","unstructured":"Weston J et al (2012) Deep learning via semi-supervised embedding. In: Neural networks: tricks of the trade. Springer, Berlin, pp 639\u2013655","DOI":"10.1007\/978-3-642-35289-8_34"},{"key":"1916_CR8","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.neucom.2014.03.077","volume":"149","author":"W Yu","year":"2015","unstructured":"Yu W et al (2015) Learning deep representations via extreme learning machines. Neurocomputing 149:308\u2013315","journal-title":"Neurocomputing"},{"key":"1916_CR9","unstructured":"Pandey G, Dukkipati A (2014) To go deep or wide in learning? In: Proceedings of the 17th international conference on artificial intelligence and statistics (AISTATS), vol 33. Reykjavik, Iceland"},{"issue":"1","key":"1916_CR10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1\u2013127","journal-title":"Found Trends Mach Learn"},{"key":"1916_CR11","doi-asserted-by":"crossref","unstructured":"Larochelle H et al (2007) An empirical evaluation of deep architectures on problems with many factors of variation. In: Proceedings of the 24th international conference on machine learning. ACM","DOI":"10.1145\/1273496.1273556"},{"key":"1916_CR12","unstructured":"Boureau Y, Cun YL (2008) Sparse feature learning for deep belief networks. In: Advances in neural information processing systems, pp 1185\u20131192"},{"key":"1916_CR13","doi-asserted-by":"crossref","unstructured":"Vincent P et al (2008) Extracting and composing robust features with denoising auto-encoders. In: Proceedings of the 25th international conference on machine learning. ACM, pp 1096\u20131103","DOI":"10.1145\/1390156.1390294"},{"key":"1916_CR14","unstructured":"Coates A, Ng AY, Lee H (2011) An analysis of single-layer networks in unsupervised feature learning. In: International conference on artificial intelligence and statistics"},{"key":"1916_CR15","doi-asserted-by":"crossref","unstructured":"Ahmed A et al (2008) Training hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks. Computer Vision-ECCV 2008. Springer, Berlin, pp 69\u201382","DOI":"10.1007\/978-3-540-88690-7_6"},{"key":"1916_CR16","doi-asserted-by":"crossref","unstructured":"Ribeiro B, Lopes N (2013) Extreme learning classifier with deep concepts. In: Progress in pattern recognition, image analysis, computer vision, and applications. Springer, Berlin, pp 182\u2013189","DOI":"10.1007\/978-3-642-41822-8_23"},{"key":"1916_CR17","first-page":"3371","volume":"11","author":"V Pascal","year":"2010","unstructured":"Pascal V et al (2010) Stacked denoising auto-encoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371\u20133408","journal-title":"J Mach Learn Res"},{"key":"1916_CR18","first-page":"350","volume":"25","author":"J Xie","year":"2012","unstructured":"Xie J, Xu L, Chen E (2012) Image denoising and inpainting with deep neural networks. Adv Neural Inf Process Syst 25:350\u2013358","journal-title":"Adv Neural Inf Process Syst"},{"issue":"6","key":"1916_CR19","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1109\/TPAMI.2002.1008382","volume":"24","author":"AM Martnez","year":"2002","unstructured":"Martnez AM (2002) Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans Pattern Anal Mach Intell 24(6):748\u2013763","journal-title":"Pattern Anal Mach Intell IEEE Trans"},{"issue":"3","key":"1916_CR20","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1109\/TPAMI.2006.46","volume":"28","author":"S Fidler","year":"2006","unstructured":"Fidler S, Skocaj D, Leonardis A (2006) Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling. IEEE Trans Pattern Anal Mach Intell 28(3):337\u2013350","journal-title":"Pattern Anal Mach Intell IEEE Trans"},{"key":"1916_CR21","unstructured":"Liu W, Pokharel PP, Principe JC (2006) Correntropy: a localized similarity measure. In: Neural networks, 2006. IJCNN 06. International joint conference on, 2006, pp 4919\u20134924"},{"key":"1916_CR22","volume-title":"Unsupervised adaptive filtering","author":"JC Principe","year":"2000","unstructured":"Principe JC, Fisher JW III, Xu D (2000) Information theoretic learning. In: Haykin S (ed) Unsupervised adaptive filtering. Wiley, New York, NY"},{"issue":"1","key":"1916_CR23","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.sigpro.2008.07.005","volume":"89","author":"A Gunduz","year":"2009","unstructured":"Gunduz A, Principe JC (2009) Correntropy as a novel measure for nonlinearity tests. Signal Process 89(1):14\u201323","journal-title":"Signal Process"},{"issue":"11","key":"1916_CR24","doi-asserted-by":"crossref","first-page":"5286","DOI":"10.1109\/TSP.2007.896065","volume":"55","author":"W Liu","year":"2007","unstructured":"Liu W, Pokharel PP, Principe JC (2007) Correntropy: properties and applications in non-Gaussian signal processing. Signal Process IEEE Trans 55(11):5286\u20135298","journal-title":"Signal Process IEEE Trans"},{"issue":"8","key":"1916_CR25","doi-asserted-by":"crossref","first-page":"2074","DOI":"10.1162\/NECO_a_00155","volume":"23","author":"R He","year":"2011","unstructured":"He R et al (2011) A regularized correntropy framework for robust pattern recognition. Neural Comput 23(8):2074\u20132100","journal-title":"Neural Comput"},{"key":"1916_CR26","doi-asserted-by":"crossref","unstructured":"Zhao S, Chen B, Principe JC (2011) Kernel adaptive filtering with maximum correntropy criterion. In: Proceedings of the international joint conference neural networks (IJCNN), pp 2012\u20132017","DOI":"10.1109\/IJCNN.2011.6033473"},{"key":"1916_CR27","doi-asserted-by":"crossref","unstructured":"Singh A, Principe JC (2010) A loss function for classification based on a robust similarity metric. In: Proceedings of the international joint conference neural networks (IJCNN), pp 1\u20136","DOI":"10.1109\/IJCNN.2010.5596485"},{"issue":"8","key":"1916_CR28","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/LSP.2014.2320944","volume":"21","author":"B Chen","year":"2014","unstructured":"Chen B, Xing L, Liang J, Zheng N, Principe JC (2014) Steady-state mean-square error analysis for adaptive filtering under the maximum correntropy criterion. IEEE Signal Process Lett 21(8):880\u2013884","journal-title":"IEEE Signal Process Lett"},{"key":"1916_CR29","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/LSP.2012.2204435","volume":"19","author":"B Chen","year":"2012","unstructured":"Chen B, Principe JC (2012) Maximum correntropy estimation is a smoothed MAP estimation. IEEE Signal Process Lett 19:491\u2013494","journal-title":"IEEE Signal Process Lett"},{"key":"1916_CR30","doi-asserted-by":"crossref","unstructured":"Seth S, Principe JC (2008) Compressed signal reconstruction using the correntropy induced metric. In: Acoustics, speech and signal processing, 2008. ICASSP 2008. IEEE international conference on. IEEE","DOI":"10.1109\/ICASSP.2008.4518492"},{"key":"1916_CR31","doi-asserted-by":"crossref","unstructured":"Singh A, Principe JC (2010) A loss function for classification based on a robust similarity metric. In: Neural networks (IJCNN), the 2010 international joint conference on. IEEE","DOI":"10.1109\/IJCNN.2010.5596485"},{"issue":"1","key":"1916_CR32","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.patcog.2013.07.017","volume":"47","author":"A Singh","year":"2014","unstructured":"Singh A, Pokharel R, Principe J (2014) The C-loss function for pattern classification. Pattern Recognit 47(1):441\u2013453","journal-title":"Pattern Recognit"},{"key":"1916_CR33","doi-asserted-by":"crossref","unstructured":"Qi Y, Wang Y, Zheng X et al (2014) Robust feature learning by stacked auto-encoder with maximum correntropy criterion. In: Acoustics, speech and signal processing (ICASSP), 2014 IEEE international conference on. IEEE","DOI":"10.1109\/ICASSP.2014.6854900"},{"issue":"11","key":"1916_CR34","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"1916_CR35","doi-asserted-by":"crossref","DOI":"10.1002\/9780470608593","volume-title":"Kernel adaptive filtering","author":"W Liu","year":"2010","unstructured":"Liu W, Principe JC, Haykin S (2010) Kernel adaptive filtering. Wiley, New York"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-1916-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-015-1916-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-015-1916-x","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T19:15:50Z","timestamp":1691608550000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-015-1916-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,4,25]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2016,5]]}},"alternative-id":["1916"],"URL":"https:\/\/doi.org\/10.1007\/s00521-015-1916-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,4,25]]}}}