NDLI: Feature extraction via multi-view non-negative matrix factorization with local graph regularization

Content Provider	IEEE Xplore Digital Library
Author	Zhenfan Wang Xiangwei Kong Haiyan Fu Ming Li Yujia Zhang
Copyright Year	2015
Description	Author affiliation: Sch. of Inf. & Commun. Eng., Dalian Univ. of Technol., Dalian, China (Zhenfan Wang; Xiangwei Kong; Haiyan Fu; Ming Li) \|\| Fordham Univ., New York, NY, USA (Yujia Zhang)
Abstract	Feature extraction is a crucial and difficult issue in pattern recognition tasks with the high-dimensional and multiple features. To extract the latent structure of multiple features without label information, multi-view learning algorithms have been developed. In this paper, motivated by manifold learning and multi-view Non-negative Matrix Factorization (NM-F), we introduce a novel feature extraction method via multi-view NMF with local graph regularization, where the inner-view relatedness between data is taken into consideration. We propose the matrix factorization objective function by constructing a nearest neighbor graph to integrate local geometrical information of each view and apply two iterative updating rules to effectively solve the optimization problem. In the experiment, we use the extracted feature to cluster several realistic datasets. The experimental results demonstrate the effectiveness of our proposed feature extraction approach.
Starting Page	3500
Ending Page	3504
File Size	589512
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351455
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Matrix decomposition Linear programming Optimization Clustering algorithms Manifolds Measurement clustering multi-view learning non-negative matrix factorization graph regularization
Content Type	Text
Resource Type	Article

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