DocumentCode :
3165931
Title :
Binary Matrix Factorization with Applications
Author :
Zhang, Zhongyuan ; Ding, Chris ; Li, Tao ; Zhang, Xiangsun
Author_Institution :
Chinese Acad. of Sci., Beijing
fYear :
2007
fDate :
28-31 Oct. 2007
Firstpage :
391
Lastpage :
400
Abstract :
An interesting problem in nonnegative matrix factorization (NMF) is to factorize the matrix X which is of some specific class, for example, binary matrix. In this paper, we extend the standard NMF to binary matrix factorization (BMF for short): given a binary matrix X, we want to factorize X into two binary matrices W, H (thus conserving the most important integer property of the objective matrix X) satisfying X ap WH. Two algorithms are studied and compared. These methods rely on a fundamental boundedness property of NMF which we propose and prove. This new property also provides a natural normalization scheme that eliminates the bias of factor matrices. Experiments on both synthetic and real world datasets are conducted to show the competency and effectiveness of BMF.
Keywords :
matrix decomposition; bias elimination; binary matrix factorization; fundamental boundedness property; integer property; natural normalization scheme; nonnegative matrix factorization; Application software; Clustering algorithms; Computer science; DNA; Data analysis; Data mining; Machine learning; Matrix decomposition; Proteins; USA Councils;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on
Conference_Location :
Omaha, NE
ISSN :
1550-4786
Print_ISBN :
978-0-7695-3018-5
Type :
conf
DOI :
10.1109/ICDM.2007.99
Filename :
4470263
Link To Document :
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