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发表刊物：IEEE Transactions on Neural Networks and Learning Systems

摘要：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.

全部作者： S.S. Dlay, W.L. Woo,Bin Gao

通讯作者：Bin Gao

学科门类：工学

卷号：23

期号：5

页面范围：703-716

是否译文：否

发表时间：2012-05-02