文章基本信息

标题：Network-incorporated integrative sparse linear discriminant analysis
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作者：Wang, Xiaoyan ; Wang, Xiaoyan ; Fang, Kuangnan 等
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2019
卷号：12
期号：1
页码：149-166
DOI：10.4310/SII.2019.v12.n1.a13
出版社：International Press
摘要：Linear discriminant analysis (LDA) has been extensively applied in classification. For high-dimensional data, results generated from a single dataset may be unsatisfactory because of the small sample size. Under the regression framework, integrative analysis, which pools and analyses raw data from multiple datasets, has presented superior performance than single dataset analysis and meta-analysis. In this study, we conduct integrative analysis for LDA (iLDA). A network structure for variables is constructed to accommodate their interconnections, which have not been considered in many of the existing classification studies. We adopt the $1$-norm group MCP method for simultaneous estimation and discriminative variable selection, and a Laplacian penalty to incorporate the network. The proposed method has intuitive formulations and can be computed using an effective coordinate descent algorithm. Simulation study shows that iLDA outperforms benchmarks with more accurate variable identification and classification. Analysis of three breast cancer datasets demonstrate that iLDA can improve prediction performance.
关键词：integrative analysis; discriminant analysis; network