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Nonnegative Matrix Factorization Using Projected Gradient Algorithms with Sparseness Constraints
KTH, Skolan för elektro- och systemteknik (EES), Ljud- och bildbehandling.
KTH, Skolan för elektro- och systemteknik (EES), Ljud- och bildbehandling.ORCID-id: 0000-0001-7957-5103
2009 (engelsk)Inngår i: 2009 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT 2009), NEW YORK: IEEE conference proceedings, 2009, s. 418-423Konferansepaper, Publicerat paper (Fagfellevurdert)
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

sted, utgiver, år, opplag, sider
NEW YORK: IEEE conference proceedings, 2009. s. 418-423
Emneord [en]
non-negative matrix factorization, projected gradient algorithms, sparseness
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-34248DOI: 10.1109/ISSPIT.2009.5407557ISI: 000290365400075Scopus ID: 2-s2.0-77749334627OAI: oai:DiVA.org:kth-34248DiVA, id: diva2:422506
Konferanse
9th IEEE International Symposium on Signal Processing and Information Technology Ajman, U ARAB EMIRATES, DEC 14-17, 2009
Merknad

QC 20110613

Tilgjengelig fra: 2011-06-13 Laget: 2011-05-30 Sist oppdatert: 2024-01-08bibliografisk kontrollert

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