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Recovering the low-rank, sparse components of a given matrix is a challenging problem that arises in many real applications. Existing traditional approaches aimed at solving this problem are usually recast as a general approximation problem of a low-rank matrix. These approaches are based on the nuclear norm of the matrix, and thus in practice the rank may not be well approximated. This paper presents...
Robust modeling approaches have received considerable attention due to its practical value to deal with the presence of outliers in data. This paper proposes a probabilistic robust learning algorithm for neural networks with random weights (NNRWs) to improve the modeling performance. The robust NNRW model is trained by optimizing a hybrid regularization loss function according to the sparsity of outliers...
Electronic commerce is fast expanding all over the world. Currently most B2C (business to customer) electronic commerce focuses on retailing and other online services, such as online banking. Compared with large-scale enterprises and organizations, small businesses find it difficult to involve in electronic commerce because the cost is too high to be affordable. The cost for electronic commerce not...
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