Variational Regularized 2-D Nonnegative Matrix Factorization

Gao, Bin, Woo, Wai Lok and Dlay, Satnam (2012) Variational Regularized 2-D Nonnegative Matrix Factorization. IEEE Transactions on Neural Networks and Learning Systems, 23 (5). pp. 703-716. ISSN 2162-237X

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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.

Item Type: Article
Uncontrolled Keywords: Audio process machine learning, nonnegative matrix factorization, single channel blind source separation, sparsity-aware learning, variational regularization
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 10 May 2019 16:24
Last Modified: 10 Oct 2019 19:02

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