NDLI: Sparse Boolean Matrix Factorizations

Content Provider	IEEE Xplore Digital Library
Author	Miettinen, P.
Copyright Year	2010
Abstract	Matrix factorizations are commonly used methods in data mining. When the input data is Boolean, replacing the standard matrix multiplication with Boolean matrix multiplication can yield more intuitive results. Unfortunately, finding a good Boolean decomposition is known to be computationally hard, with even many sub-problems being hard to approximate. Many real-world data sets are sparse, and it is often required that also the factor matrices are sparse. This requirement has motivated many new matrix decomposition methods and many modifications of the existing methods. This paper studies how Boolean matrix factorizations behave with sparse data: can we assume some sparsity on the factor matrices, and does the sparsity help with the computationally hard problems. The answer to these problems is shown to be positive.
Starting Page	935
Ending Page	940
File Size	232470
Page Count	6
File Format	PDF
ISBN	9781424491315
ISSN	15504786
DOI	10.1109/ICDM.2010.93
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-13
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sparse matrices Approximation methods Matrix decomposition Approximation algorithms Data mining Noise Linear matrix inequalities approximation algorithms Matrix decompositions Boolean rank
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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