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Variational Regularized 2-D Nonnegative Matrix Factorization
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DOI：10.1109/TNNLS.2012.2187925.png)
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Abstract
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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.
Keywords:
Audio process machine learning
nonnegative matrix factorization
single channel blind source separation
sparsity-aware learning
variational regularization
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