DocumentCode
2373253
Title
Convergence-guaranteed multiplicative algorithms for nonnegative matrix factorization with β-divergence
Author
Nakano, Masahiro ; Kameoka, Hirokazu ; Le Roux, Jonathan ; Kitano, Yu. ; Ono, Nobutaka ; Sagayama, Shigeki
Author_Institution
Grad. Sch. of Inf. Sci. & Technol., Univ. of Tokyo, Tokyo, Japan
fYear
2010
fDate
Aug. 29 2010-Sept. 1 2010
Firstpage
283
Lastpage
288
Abstract
This paper presents a new multiplicative algorithm for nonnegative matrix factorization with β-divergence. The derived update rules have a similar form to those of the conventional multiplicative algorithm, only differing through the presence of an exponent term depending on β. The convergence is theoretically proven for any real-valued β based on the auxiliary function method. The convergence speed is experimentally investigated in comparison with previous works.
Keywords
convergence; matrix decomposition; matrix multiplication; β-divergence; convergence-guaranteed multiplicative algorithms; nonnegative matrix factorization; Book reviews; Convergence; Maximum likelihood estimation; Minimization; Signal processing algorithms; Source separation;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop on
Conference_Location
Kittila
ISSN
1551-2541
Print_ISBN
978-1-4244-7875-0
Electronic_ISBN
1551-2541
Type
conf
DOI
10.1109/MLSP.2010.5589233
Filename
5589233
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