文献类型：期刊文献

英文题名：A NOVEL DISCRIMINANT MINIMUM CLASS LOCALITY PRESERVING CANONICAL CORRELATION ANALYSIS AND ITS APPLICATIONS

作者：Yuan, Yubo[1,3];Ma, Chenglong[1,2];Pu, Dongmei[1,3]

机构：[1]China Jiliang Univ, Inst Metrol & Computat Sci, Hangzhou 310018, Zhejiang, Peoples R China;[2]Wonders Informat Corp Ltd, Ctr Res & Innovat, Shanghai 201112, Peoples R China;[3]E China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2016

卷号：12

期号：1

起止页码：251

外文期刊名：JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000358690700014)】；

基金：This research has been supported by the National Natural Science Foundation under Grant(No. 61001200) and Natural Science Foundation and Education Department of Zhejiang under Grants(Nos.Y6100010).

语种：英文

外文关键词：Canonical correlation analysis; locality preserving; global discriminant; dimensionality reduction

摘要：Canonical correlation analysis(CCA) is a well-known technique for simultaneously reducing two relevant data sets, and finding maximal correlation between them. However, it fails to preserve the local structure of each data set, as well as the global discriminant ability, which are important in real applications. In this paper, a new CCA model, called discriminant minimum class locality preserving canonical correlation analysis(called as DMPCCA) is proposed. The proposed method introduces locall structure information and global discriminant information into the classical CCA and considers a optimal combination of intra-class locality preserving, global discriminant ability and the maximal correlation between two sets. The experiments on data visualization, web image retrieval and face recognition validate the effectiveness of the proposed method.