Accelerated stochastic multiplicative update with gradient averaging for nonnegative matrix factorizations

2018 ◽  
Author(s):  
Hiroyuki Kasai
Keyword(s):  
Nonnegative Matrix ◽  
Matrix Factorizations ◽  
Multiplicative Update

A framework for least squares nonnegative matrix factorizations with Tikhonov regularization

2020 ◽  
Vol 387 ◽  
pp. 78-90
Author(s):  
Yueyang Teng ◽  
Shouliang Qi ◽  
Fangfang Han ◽  
Yudong Yao ◽  
Fenglei Fan ◽  
...  
Keyword(s):  
Least Squares ◽  
Tikhonov Regularization ◽  
Nonnegative Matrix ◽  
Matrix Factorizations

scholarly journals Projected Gradient Methods for Nonnegative Matrix Factorization

2007 ◽  
Vol 19 (10) ◽  
pp. 2756-2779 ◽  
Author(s):  
Chih-Jen Lin
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Gradient Methods ◽  
Nonnegative Matrix ◽  
Theory And Practice ◽  
Bound Constraints ◽  
Projected Gradient ◽  
Matlab Code ◽  
Projected Gradient Methods ◽  
Multiplicative Update

Nonnegative matrix factorization (NMF) can be formulated as a minimization problem with bound constraints. Although bound-constrained optimization has been studied extensively in both theory and practice, so far no study has formally applied its techniques to NMF. In this letter, we propose two projected gradient methods for NMF, both of which exhibit strong optimization properties. We discuss efficient implementations and demonstrate that one of the proposed methods converges faster than the popular multiplicative update approach. A simple Matlab code is also provided.

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