Title of Paper:Variational Bayesian Regularized Two-Dimensional Nonnegative Matrix Factorization

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Journal:IEEE Transactions on Neural Networks and Learning Systems

Abstract:A novel approach for adaptive regularization of two-dimensional nonnegative matrix factorization is presented. The proposed matrix factorization is developed under the framework of maximum a posteriori probability and is adaptively fine-tuned using the variational approach. The method enables (i) a generalized criterion for variable sparseness to be imposed onto the solution and (ii) prior information to be explicitly incorporated into the basis features. The method is computationally efficient and has been demonstrated on applications i.e. extracting features from image.

All the Authors: S.S. Dlay, W.L. Woo,Bin Gao

Correspondence Author:Bin Gao

Discipline:Engineering

Volume:23

Issue:5

Page Number:703-716

Translation or Not:no

Date of Publication:2012-05-02