Gene Extraction Based on Sparse Singular Value Decomposition

Xiangzhen Kong,Chunhou Zheng,Junliang Shang,Jinxing Liu

doi:10.1007/978-3-319-42291-6_28

Gene Extraction Based on Sparse Singular Value Decomposition

Xiangzhen Kong, Chunhou Zheng + Show 2 more

https://doi.org/10.1007/978-3-319-42291-6_28

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Publication Date: Jan 1, 2016

Citations: 1

Affiliation: Qufu Normal University, Harbin Institute of Technology

#Sparse Singular Value Decomposition #Existing Feature Extraction Methods + Show 8 more

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Abstract

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.

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