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Joint L(1/2)-Norm Constraint and Graph-Laplacian PCA Method for Feature Extraction

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 algori...

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Detalles Bibliográficos
Autores principales: Feng, Chun-Mei, Gao, Ying-Lian, Liu, Jin-Xing, Wang, Juan, Wang, Dong-Qin, Wen, Chang-Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5392409/
https://www.ncbi.nlm.nih.gov/pubmed/28470011
http://dx.doi.org/10.1155/2017/5073427
Descripción
Sumario: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 L(1/2) constraint (L(1/2) gLPCA) on error function for feature (gene) extraction. The error function based on L(1/2)-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.