期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 41, 期 1, 页码 246-259出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2017.2777841
关键词
Non-negative matrix factorization; truncated cauchy loss; robust statistics; half-quadratic programming
资金
- Australian Research Council [FL-170100117, DP-180103424, DP-140102164, LP-150100671]
- National Natural Science Foundation of China [61502515]
Non-negative matrix factorization (NMF) minimizes the euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated CauchyNMF loss that handle outliers by truncating large errors, and develop a Truncated CauchyNMF to robustly learn the subspace on noisy datasets contaminated by outliers. We theoretically analyze the robustness of Truncated CauchyNMF comparing with the competing models and theoretically prove that Truncated CauchyNMF has a generalization bound which converges at a rate of order O(root ln n/n), where n is the sample size. We evaluate Truncated CauchyNMF by image clustering on both simulated and real datasets. The experimental results on the datasets containing gross corruptions validate the effectiveness and robustness of Truncated CauchyNMF for learning robust subspaces.
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