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• W2574182783 abstract "Robust Principal Component Analysis (RPCA) is an efficient method in the selection of differentially expressed genes. However, nuclear norm minimizes all singular values simultaneously, so it may not be the best solution to replace the low-rank function. In this paper, the truncated nuclear norm is introduced. And a new method named Truncated nuclear norm regularized Robust Principal Component Analysis (TRPCA) is proposed. The method decomposes the observation matrix of genomic data into a low-rank matrix and a sparse matrix. The differentially expressed genes can be selected according to the sparse matrix. The experimental results on the The Cancer Genome Atlas (TCGA) data illustrate that the TRPCA method outperforms other state-of-the-art methods in the selection of differentially expressed genes." @default.
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• W2574182783 date "2016-12-01" @default.
• W2574182783 modified "2023-09-26" @default.
• W2574182783 title "Differentially expressed genes selection via Truncated Nuclear Norm Regularization" @default.
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• W2574182783 doi "https://doi.org/10.1109/bibm.2016.7822800" @default.
• W2574182783 hasPublicationYear "2016" @default.
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