NDLI: Robust Non-negative Graph Embedding: Towards noisy data, unreliable graphs, and noisy labels

Content Provider	IEEE Xplore Digital Library
Author	Hanwang Zhang Zheng-Jun Zha Shuicheng Yan Meng Wang Tat-Seng Chua
Copyright Year	2012
Description	Author affiliation: School of Computing, National University of Singapore (Hanwang Zhang; Zheng-Jun Zha; Meng Wang; Tat-Seng Chua) \|\| Electrical Computer Engineering, National University of Singapore (Shuicheng Yan)
Abstract	Non-negative data factorization has been widely used recently. However, existing techniques, such as Non-negative Graph Embedding (NGE), often suffer from noisy data, unreliable graphs, and noisy labels, which are commonly encountered in real-world applications. To address these issues, in this paper, we propose a Robust Non-negative Graph Embedding (RNGE) framework. The joint sparsity in both graph embedding and reconstruction endues the robustness of RNGE. We develop an elegant multiplicative updating solution that can solve RNGE efficiently and prove the convergence rigourously. RNGE is robust to unreliable graphs, as well as both sample and label noises in training data. Moreover, RNGE provides a general formulation such that all the algorithms unified with the graph embedding framework can be easily extended to obtain their robust non-negative solutions. We conduct extensive experiments on four real-world datasets and compared the proposed RNGE to NGE and other representative non-negative data factorization and subspace learning methods. The experimental results demonstrate the effectiveness and robustness of RNGE.
Starting Page	2464
Ending Page	2471
File Size	328711
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6247961
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise measurement Robustness Noise Vectors Convergence Algorithm design and analysis Principal component analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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