Generalization Bounds For Unsupervised and Semi-Supervised Learning With Autoencoders.
作者： Ron Meir , Baruch Epstein
DOI:
关键词:
摘要: Autoencoders are widely used for unsupervised learning and as a regularization scheme in semi-supervised learning. However, theoretical understanding of their generalization …
harvard.edu
arxiv.org参考文章(16)
Matthias Seeger, Learning with Labeled and Unlabeled Data ,(2000)
Brendan van Rooyen, Robert C. Williamson, A Theory of Feature Learning arXiv: Machine Learning. ,(2015)
Joshua B Tenenbaum, Vin de Silva, John C Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction Science. ,vol. 290, pp. 2319- 2323 ,(2000) , 10.1126/SCIENCE.290.5500.2319
Sam T Roweis, Lawrence K Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding Science. ,vol. 290, pp. 2323- 2326 ,(2000) , 10.1126/SCIENCE.290.5500.2323
Joel A. Tropp, User-Friendly Tail Bounds for Sums of Random Matrices Foundations of Computational Mathematics. ,vol. 12, pp. 389- 434 ,(2012) , 10.1007/S10208-011-9099-Z
John D. Lafferty, Larry Wasserman, Statistical Analysis of Semi-Supervised Regression neural information processing systems. ,vol. 20, pp. 801- 808 ,(2007)
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition Proceedings of the IEEE. ,vol. 86, pp. 2278- 2324 ,(1998) , 10.1109/5.726791
Aarti Singh, Xiaojin Zhu, Robert Nowak, Unlabeled data: Now it helps, now it doesn't neural information processing systems. ,vol. 21, pp. 1513- 1520 ,(2008)
Partha Niyogi, Manifold regularization and semi-supervised learning: some theoretical analyses Journal of Machine Learning Research. ,vol. 14, pp. 1229- 1250 ,(2013)
Marc' Aurelio Ranzato, Martin Szummer, Semi-supervised learning of compact document representations with deep networks Proceedings of the 25th international conference on Machine learning - ICML '08. pp. 792- 799 ,(2008) , 10.1145/1390156.1390256