Browse by author
Lookup NU author(s): Dr Bin Gao, Dr Wai Lok Woo, Emeritus Professor Satnam Dlay
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
A novel approach for adaptive regularization of 2-D 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: 1) a generalized criterion for variable sparseness to be imposed onto the solution; and 2) prior information to be explicitly incorporated into the basis features. The method is computationally efficient and has been demonstrated on two applications, that is, extracting features from image and separating single channel source mixture. In addition, it is shown that the basis features of an information-bearing matrix can be extracted more efficiently using the proposed regularized priors. Experimental tests have been rigorously conducted to verify the efficacy of the proposed method.
Author(s): Gao B, Woo WL, Dlay SS
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Neural Networks and Learning Systems
Year: 2012
Volume: 23
Issue: 5
Pages: 703-716
Print publication date: 27/04/2012
ISSN (print): 2162-237X
ISSN (electronic): 2162-2388
Publisher: IEEE
URL: http://dx.doi.org/10.1109/TNNLS.2012.2187925
DOI: 10.1109/TNNLS.2012.2187925
Altmetrics provided by Altmetric
