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Author Zhongyuan Zhang ♦ Ding, C. ♦ Tao Li ♦ Xiangsun Zhang
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2007
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Computer programming, programs & data
Subject Keyword Matrix decomposition ♦ Data mining ♦ Computer science ♦ USA Councils ♦ Clustering algorithms ♦ Application software ♦ Data analysis ♦ DNA ♦ Proteins ♦ Machine learning
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.
Description Author affiliation: Chinese Acad. of Sci., Beijing (Zhongyuan Zhang)
ISBN 9780769530185
ISSN 15504786
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2007-10-28
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 224.70 kB
Page Count 10
Starting Page 391
Ending Page 400

Source: IEEE Xplore Digital Library