Gene selection for microarray data classification via adaptive hypergraph embedded dictionary learning

Abstract

Due to the rapid development of DNA microarray technology, a large number of microarray data come into being and classifying these data has been verified useful for cancer diagnosis, treatment and prevention. However, microarray data classification is still a challenging task since there are often a huge number of genes but a small number of samples in gene expression data. As a result, a computational method for reducing the dimension of microarray data is necessary. In this paper, we introduce a computational gene selection model for microarray data classification via adaptive hypergraph embedded dictionary learning (AHEDL). Specifically, a dictionary is learned from the feature space of original high dimensional microarray data, and this learned dictionary is used to represent original genes with a reconstruction coefficient matrix. Then we use a l2, 1-norm regularization to impose the row sparsity on the coefficient matrix for selecting discriminate genes. Meanwhile, in order to capture the localmanifold geometrical structure of original microarray data in a high-order manner, a hypergraph is adaptively learned and embedded into the model. An iterative updating algorithm is designed for solving the optimization problem. In order to validate the efficacy of the proposed model, we have conducted experiments on six publicly available microarray data sets and the results demonstrate that AHEDL outperforms other state-of-the-art methods in terms of microarray data classification. AbbreviationsUnlabelled TableAHEDLAdaptive Hypergraph Embedded Dictionary LearningADMMAlternating Direction method of MultipliersSVMSupport Vector MachineRFRandom Forestk-NNk-Nearest NeighborCVcross validationMSVM-RFMulticlass Support Vector Machine-Recursive Feature EliminationKernelPLSKernel Partial Least SquaresWLMGSWeight Local Modularity based Gene SelectionGRSL-GSGene Selection via Subspace Learning and Manifold RegularizationLNNFWLocal-Nearest-Neighbors-based Feature Weighting for Gene SelectionRLRRegularized Logistic RegressionACCaccuracySDstandard deviationsANOVAAnalysis of VarianceDFDegrees of FreedomSSSum-of-SquareMSMean Sum-of-SquareFF-valueSigstatistical significanceSRBCTSmall Round Blue Cell TumorsGCMGlobal Cancer MapCLL_SUB_111B-cell chronic lymphocytic leukemia