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

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 l 2, 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. Unlabelled Table AHEDL Adaptive Hypergraph Embedded Dictionary Learning ADMM Alternating Direction method of Multipliers SVM Support Vector Machine RF Random Forest k-NN k-Nearest Neighbor CV cross validation MSVM-RF Multiclass Support Vector Machine-Recursive Feature Elimination KernelPLS Kernel Partial Least Squares WLMGS Weight Local Modularity based Gene Selection GRSL-GS Gene Selection via Subspace Learning and Manifold Regularization LNNFW Local-Nearest-Neighbors-based Feature Weighting for Gene Selection RLR Regularized Logistic Regression ACC accuracy SD standard deviations ANOVA Analysis of Variance DF Degrees of Freedom SS Sum-of-Square MS Mean Sum-of-Square F F-value Sig statistical significance SRBCT Small Round Blue Cell Tumors GCM Global Cancer Map CLL_SUB_111 B-cell chronic lymphocytic leukemia • We introduce a new gene selection model for microarray data classification. • A dictionary is learned to reconstruct original genes. • A hypergraph is adaptively learned and embedded into the model. • The hypergraph is used to capture the high-order locality of microarray data. • Experiments on six data sets validate the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]