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Supervised Penalty matrix Decomposition for Tumor Differentially Expressed Genes Selection

Chinese Journal of Electronics 2018年第4期27卷 845-851页

作者： LIU Jian CHENG Yuhu WANG Xuesong CUI Xiaoluo School of Information and Control Engineering China University of Mining and Technology Department of Information Engineering Weihai Ocean Vocational College

A reliable and precise recognition of the differentially expressed genes of tumor is crucial to treat the cancer effectively. The small number of differentially expressed genes in a huge gene expression dataset determines the important role of sparse methods, such as Penalty matrix decomposition(PMD), among the feature selection methods. The sparse methods always have the drawback: they do not take advantage of known class labels of gene expression data. A novel supervised-sparse method named as Supervised PMD(SPMD) is proposed by adding the class information into PMD via the total scatter matrix. The brief idea of our method used to select the differentially expressed genes is given as follows.The total scatter matrix is obtained according to the gene expression data with class label. The obtained total scatter matrix is decomposed by PMD to acquire the sparse vectors. The non-zero items in sparse vectors are selected as the differentially expressed genes. The Gene ontology(GO) enrichment of functional annotation of the selected genes is detected by Topp Fun. Experiments on synthetic data and two real tumor gene expression datasets show that the proposed SPMD is quite promising to select the differentially expressed genes.

关键词： Tumor gene expression data Differentially expressed genes Supervised methods scatter matrix Penalized matrix decomposition(PMD)

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An Efficient Algorithm of Solving the Optimal Discriminant Vectors

An Efficient Algorithm of Solving the Optimal Discriminant V...

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2012 IEEE International Conference on Computer Science and Automation Engineering（CSAE 2012）

作者： Hong-zhou He Ming-tian Zhou School of Computer Science and Engineering University of Electronic Science & Technology of China Chengdu China Hong-zhou He College of Mathematics & Computer Science Mianyang Normal University Mianyang China

In view of the limitations of traditional uncorrelated Linear Discriminant Analysis （uLDA） of failure with singular within-scatter matrix and computationally expensive in solving the optimal discriminant vectors for a large and high-dimension dataset,an equivalent uLDA to Linear Discriminant Analysis （IDA） and a algorithm of uLDA based on generalized singular value decomposition is proposed to simply the computation and get over the singularity problem.The classification experimental results of four image and text datasets demonstrate the superiority of our algorithmover other traditional algorithms.

关键词： discriminant vector scatter matrix uLDA gSVD

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