Projective robust nonnegative factorization

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• 作者：Yuwu Lu^a ; Zhihui Lai^b ; Yong Xu^c ; ^{yongxu@ymail.com" class="auth_mail" title="E-mail the corresponding author} ; Jane You^d ; Xuelong Li^e ; Chun Yuan^a
• 关键词：Robust ; Nonnegative matrix factorization ; Graph regularization ; Face recognition
• 刊名：Information Sciences
• 出版年：2016
• 出版时间：10 October 2016
• 年：2016
• 卷：364-365
• 期：Complete
• 页码：16-32
• 全文大小：1502 K

文摘

Nonnegative matrix factorization (NMF) has been successfully used in many fields as a low-dimensional representation method. Projective nonnegative matrix factorization (PNMF) is a variant of NMF that was proposed to learn a subspace for feature extraction. However, both original NMF and PNMF are sensitive to noise and are unsuitable for feature extraction if data is grossly corrupted. In order to improve the robustness of NMF, a framework named Projective Robust Nonnegative Factorization (PRNF) is proposed in this paper for robust image feature extraction and classification. Since learned projections can weaken noise disturbances, PRNF is more suitable for classification and feature extraction. In order to preserve the geometrical structure of original data, PRNF introduces a graph regularization term which encodes geometrical structure. In the PRNF framework, three algorithms are proposed that add a sparsity constraint on the noise matrix based on L_1/2 norm, L₁ norm, and L_{2, 1} norm, respectively. Robustness and classification performance of the three proposed algorithms are verified with experiments on four face image databases and results are compared with state-of-the-art robust NMF-based algorithms. Experimental results demonstrate the robustness and effectiveness of the algorithms for image classification and feature extraction.