title: Sample-space-based feature extraction and class preserving projection for gene expression data.
abstract: In order to overcome the problems of high computational complexity and serious matrix singularity for feature extraction using Principal Component Analysis (PCA) and Fisher's Linear Discrinimant Analysis (LDA) in high-dimensional data, sample-space-based feature extraction is presented, which transforms the computation procedure of feature extraction from gene space to sample space by representing the optimal transformation vector with the weighted sum of samples. The technique is used in the implementation of PCA, LDA, Class Preserving Projection (CPP) which is a new method for discriminant feature extraction proposed, and the experimental results on gene expression data demonstrate the effectiveness of the method.
authors.affiliation: School of Computer Science and Engineering, Xidian University, Post Box 161, #2 TaiBai Road, Xi'an, 710071, China. xidianwwj219@163.com
authors.forename: Wenjun
authors.initials: W
authors.lastname: Wang
mesh_terms: D016247:Information Storage and Retrieval; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D059467:Transcriptome