PCA Based on Graph Laplacian Regularization and P-Norm for Gene Selection and Clustering

Abstract

In modern molecular biology, the hotspots and difficulties of this field are identifying characteristic genes from gene expression data. Traditional reconstruction-error-minimization model principal component analysis (PCA) as a matrix decomposition method uses quadratic error function, which is known sensitive to outliers and noise. Hence, it is necessary… (More)
DOI: 10.1109/TNB.2017.2690365

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Cite this paper

@article{Feng2017PCABO, title={PCA Based on Graph Laplacian Regularization and P-Norm for Gene Selection and Clustering}, author={Chun-Mei Feng and Ying-Lian Gao and Jin-Xing Liu and Chun-Hou Zheng and Jiguo Yu}, journal={IEEE Transactions on NanoBioscience}, year={2017}, volume={16}, pages={257-265} }