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Nonnegative Matrix Factorization Using Projected Gradient Algorithms with Sparseness Constraints
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
2009 (English)In: 2009 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT 2009), NEW YORK: IEEE conference proceedings, 2009, 418-423 p.Conference paper (Refereed)
Abstract [en]

Recently projected gradient (PG) approaches have found many applications in solving the minimization problems underlying nonnegative matrix factorization (NMF). NMF is a linear representation of data that could lead to sparse result of natural images. To improve the parts-based representation of data some sparseness constraints have been proposed. In this paper the efficiency and execution time of five different PG algorithms and the basic multiplicative algorithm for NMF are compared. The factorization is done for an existing and proposed sparse NMF and the results are compared for all these PG methods. To compare the algorithms the resulted factorizations are used for a hand-written digit classifier

Place, publisher, year, edition, pages
NEW YORK: IEEE conference proceedings, 2009. 418-423 p.
Keyword [en]
non-negative matrix factorization, projected gradient algorithms, sparseness
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-34248DOI: 10.1109/ISSPIT.2009.5407557ISI: 000290365400075ScopusID: 2-s2.0-77749334627OAI: diva2:422506
9th IEEE International Symposium on Signal Processing and Information Technology Ajman, U ARAB EMIRATES, DEC 14-17, 2009

QC 20110613

Available from: 2011-06-13 Created: 2011-05-30 Last updated: 2012-11-01Bibliographically approved

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