Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/36174
Title: Convex nonnegative matrix factorization with manifold regularization
Authors: Hu, WJ
Choi, KS 
Wang, PL
Jiang, YL
Wang, ST
Keywords: Nonnegative matrix factorization
Manifold regularization
Convex nonnegative matrix factorization
Clustering
Issue Date: 2015
Publisher: Pergamon Press
Source: Neural networks, 2015, v. 63, p. 94-103 How to cite?
Journal: Neural networks 
Abstract: Nonnegative Matrix Factorization (NMF) has been extensively applied in many areas, including computer vision, pattern recognition, text mining, and signal processing. However, nonnegative entries are usually required for the data matrix in NMF, which limits its application. Besides, while the basis and encoding vectors obtained by NMF can represent the original data in low dimension, the representations do not always reflect the intrinsic geometric structure embedded in the data. Motivated by manifold learning and Convex NMF (CNMF), we propose a novel matrix factorization method called Graph Regularized and Convex Nonnegative Matrix Factorization (GCNMF) by introducing a graph regularized term into CNMF. The proposed matrix factorization technique not only inherits the intrinsic low-dimensional manifold structure, but also allows the processing of mixed-sign data matrix. Clustering experiments on nonnegative and mixed-sign real-world data sets are conducted to demonstrate the effectiveness of the proposed method.
URI: http://hdl.handle.net/10397/36174
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2014.11.007
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