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BioMed Research International
Volume 2017, Article ID 5073427, 14 pages
Research Article

Joint -Norm Constraint and Graph-Laplacian PCA Method for Feature Extraction

1School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China
2Library of Qufu Normal University, Qufu Normal University, Rizhao 276826, China

Correspondence should be addressed to Ying-Lian Gao; moc.621@oagnailniy

Received 30 December 2016; Revised 12 February 2017; Accepted 1 March 2017; Published 2 April 2017

Academic Editor: Jialiang Yang

Copyright © 2017 Chun-Mei Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adopting constraint ( gLPCA) on error function for feature (gene) extraction. The error function based on -norm helps to reduce the influence of outliers and noise. Augmented Lagrange Multipliers (ALM) method is applied to solve the subproblem. This method gets better results in feature extraction than other state-of-the-art PCA-based methods. Extensive experimental results on simulation data and gene expression data sets demonstrate that our method can get higher identification accuracies than others.