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
Link To Document :
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