Gene Extraction Based on Sparse Singular Value Decomposition
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In this paper, we develop a new feature extraction method based on sparse singular value decomposition (SSVD). We apply SSVD algorithm to select the characteristic genes from Colorectal Cancer (CRC) genomic dataset, and then the differentially expressed genes obtained are evaluated by the tools based on Gene Ontology. As a gene extraction method, SSVD is also compared with some existing feature extraction methods such as independent component analysis (ICA), the p-norm robust feature extraction (PREE) and sparse principal component analysis (SPCA). The experimental results show that SSVD method outperforms the existing algorithms.
KeywordsSingular value decomposition Gene extraction Sparse constraint Gene Ontology
This work was supported in part by the grants of the National Science Foundation of China, Nos. 61572284, 61502272, 61572283; Shenzhen Municipal Science and Technology Innovation Council, No. JCYJ20140417172417174; Natural Science Foundation of Shandong Province, No. BS2014DX004.
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