Atomic Decomposition by Basis Pursuit
SIAM Review
Gene selection using a two-level hierarchical Bayesian model
Bioinformatics
Cancer gene search with data-mining and genetic algorithms
Computers in Biology and Medicine
Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Feature Extraction and Uncorrelated Discriminant Analysis for High-Dimensional Data
IEEE Transactions on Knowledge and Data Engineering
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Discriminant Analysis for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computers in Biology and Medicine
Gene Selection Using l1-Norm Least Square Regression
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 05
Multiclass Gene Selection on Microarray Data Using l1-norm Least Square Regression
IJCBS '09 Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing
Tumor clustering using nonnegative matrix factorization with gene selection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Two-dimensional maximum margin feature extraction for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Feature Selection for Gene Expression Using Model-Based Entropy
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Nonparametric Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
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Dimensionality reduction is necessary for gene expression data classification. In this paper, we propose a new method for reducing the dimensionality of gene expression data. First, based on a sparse representation, we developed a new criterion for characterizing the margin, which is called sparse maximum margin discriminant analysis (SMMDA); this approach can be used to find an optimal transform matrix such that the sparse margin is maximal in the transformed space. Second, using SMMDA, we present a new feature extraction method for gene expression data. Third, based on SMMDA, we propose a new discriminant gene selection method. During gene selection, we first found the one-dimensional projection of the gene expression data in the most separable direction using SMMDA. Then, we applied the sparse representation technique to regress the projection, and we obtained the relevance vector for the gene set. Discriminant genes were then selected according to this vector. Compared with the conventional method of maximum margin discriminant analysis, the proposed SMMDA method successfully avoids the difficulty of parameter selection. Extensive experiments using publicly available gene expression datasets showed that SMMDA is efficient for feature extraction and gene selection.