Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 19, Issue 4, Pages 2420-2430Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2021.3065054
Keywords
Principal component analysis; Cancer; Gene expression; Robustness; Linear programming; Marine vehicles; Information science; Robust principal component analysis; sample clustering; co-characteristic gene selection; L2; 1-norm; hypergraph regularization
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Funding
- NSFC [61872220, 61572284]
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A new model called Robust Principal Component Analysis via Hypergraph Regularization (HRPCA) is proposed in this paper. HRPCA utilizes L2,1-norm to reduce the effect of outliers and make data sufficiently row-sparse. The hypergraph regularization is introduced to consider the complex relationship among data. Important information hidden in the data are mined, and this method ensures the accuracy of the resulting data relationship information.
Extracting genes involved in cancer lesions from gene expression data is critical for cancer research and drug development. The method of feature selection has attracted much attention in the field of bioinformatics. Principal Component Analysis (PCA) is a widely used method for learning low-dimensional representation. Some variants of PCA have been proposed to improve the robustness and sparsity of the algorithm. However, the existing methods ignore the high-order relationships between data. In this paper, a new model named Robust Principal Component Analysis via Hypergraph Regularization (HRPCA) is proposed. In detail, HRPCA utilizes L2,1-norm to reduce the effect of outliers and make data sufficiently row-sparse. And the hypergraph regularization is introduced to consider the complex relationship among data. Important information hidden in the data are mined, and this method ensures the accuracy of the resulting data relationship information. Extensive experiments on multi-view biological data demonstrate that the feasible and effective of the proposed approach.
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