Joint L1/2-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...
| Published in: | BioMed Research International Vol. 2017; pp. 1 - 15 |
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| Main Authors: | , , , , , |
| Format: | algorithm equations & formulas research Journal Article |
| Published: |
Wiley-Blackwell
4/2/2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=122257290&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 122257290 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/2/2017 vid: 2017 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 122257290 122257290 122257290 10.1155/2017/5073427 122257290 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Joint L1/2-Norm Constraint and Graph-Laplacian PCA Method for Feature Extraction. aug: au: Feng, Chun-Mei Gao, Ying-Lian Liu, Jin-Xing Wang, Juan Wang, Dong-Qin Wen, Chang-Gang affil: School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China sug: subj: Genes Bioinformatics Evaluation Graphics Gene Expression Sequence Analysis Human Funding Source ab: 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 L1/2 constraint (L1/2 gLPCA) on error function for feature (gene) extraction. The error function based on L1/2-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. pubtype: Academic Journal doctype: algorithm equations & formulas research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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