DocumentCode :
2924037
Title :
Non-negative Matrix Factorization with sparse features
Author :
Kimura, Keigo ; Yoshida, Tetsuya
Author_Institution :
Grad. Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo, Japan
fYear :
2011
fDate :
8-10 Nov. 2011
Firstpage :
324
Lastpage :
329
Abstract :
We propose an approach for Non-negative Matrix Factorization (NMF) with sparseness constraints on feature vectors. It has been believed that the non-negativity constraint in NMF contributes to making the learned features sparse, and some approaches incorporated additional sparseness constraints. However, previous approaches have not considered the sparsity of features explicitly. Our approach explicitly incorporates the notion of sparsity of features, in terms of independence of features and correlation of features. The proposed notion of sparsity is formalized as regularization terms in the framework of NMF, and learning algorithms with multiplicative update rules are proposed. The proposed approach is evaluated in terms of document clustering over well-known benchmark datasets. The results are encouraging and show that the proposed approach improves the clustering performance, while sustaining relatively good quality of data approximation.
Keywords :
constraint handling; correlation methods; document handling; learning (artificial intelligence); matrix decomposition; pattern clustering; set theory; NMF; benchmark datasets; data approximation; document clustering performance; feature correlation; learning algorithm; nonnegative matrix factorization; nonnegativity constraint; regularization term; sparse feature vector; sparseness constraint; Approximation algorithms; Approximation methods; Clustering algorithms; Correlation; Feature extraction; Sparse matrices; Vectors; Clustering; Localized representation; Non-negative Matrix Factorization; Sparse Coding;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing (GrC), 2011 IEEE International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-1-4577-0372-0
Type :
conf
DOI :
10.1109/GRC.2011.6122616
Filename :
6122616
Link To Document :
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