Kernel Generalized Canonical Correlation Analysis

作     者：Tenenhaus, Arthur Philippe, Cathy Frouin, Vincent 

作者机构：Univ Paris 11 UMR CNRS 8506 Cent Supelec Lab Signaux & Syst F-91190 Gif Sur Yvette France Brain & Spine Inst Bioinformat Biostat Platform IHU A ICM Paris France Univ Paris 11 CNRS UMR8203 Inst Gustave Roussy Villejuif France CEA Saclay NEUROSPIN I2BM F-91191 Gif Sur Yvette France 

出 版 物：《COMPUTATIONAL STATISTICS & DATA ANALYSIS》 (计算统计学与数据分析)

年 卷 期：2015年第90卷

页      面：114-131页

核心收录：

学科分类：08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：French National Research Agency (ANR GENIM) [ANR-10-BLAN-0128] French National Research Agency (ANR Investissement d'Avenir BRAINOMICS) [ANR-10-BINF-04] Agence Nationale de la Recherche (ANR) [ANR-10-BLAN-0128] Funding Source: Agence Nationale de la Recherche (ANR) 

主　　题：Regularized Generalized Canonical Correlation analysis Reproducing Kernel Hilbert Space Data integration 

摘      要：There is a growing need to analyze datasets characterized by several sets of variables observed on a single set of observations. Such complex but structured dataset are known as multiblock dataset, and their analysis requires the development of new and flexible tools. For this purpose, Kernel Generalized Canonical Correlation Analysis (KGCCA) is proposed and offers a general framework for multiblock data analysis taking into account an a priori graph of connections between blocks. It appears that KGCCA subsumes, with a single monotonically convergent algorithm, a remarkably large number of well-known and new methods as particular cases. KGCCA is applied to a simulated 3-block dataset and a real molecular biology dataset that combines Gene Expression data, Comparative Genomic Hybridization data and a qualitative phenotype measured for a set of 53 children with glioma. KGCCA is available on CRAN as part of the RGCCA package. (C) 2015 Elsevier B.V. All rights reserved.