title: Dimensionality reduction for microarray data using local mean based discriminant analysis.
abstract: A new method is proposed for finding a low dimensional subspace of high dimensional microarray data. We developed a new criterion for constructing the weight matrix by using local neighborhood information to discover the intrinsic discriminant structure in the data. Also this approach applies regularized least square technique to extract relevant features. We assess the performance of the proposed methodology by applying it to four publicly available tumor datasets. In a low dimensional subspace, the proposed method classified these tumors accurately and reliably. Also, through a comparison study, we verify the reliability of the dimensionality reduction and discrimination results.
authors.affiliation: School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu, China. yancui128@gmail.com
authors.forename: Yan
authors.initials: Y
authors.lastname: Cui
authors.forename: Chun-Hou
authors.initials: CH
authors.lastname: Zheng
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
mesh_terms: D016002:Discriminant Analysis; D006801:Humans; D046228:Microarray Analysis / Q000379:methods*; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis; D057089:Pathology, Molecular / Q000379:methods*