Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods

被引:64
|
作者
Tenenhaus, Michel [1 ]
Tenenhaus, Arthur [2 ,3 ]
Groenen, Patrick J. F. [4 ]
机构
[1] HEC Paris, Jouy En Josas, France
[2] Univ Paris Sud, L2S, Cent Supelec, Gif Sur Yvette, France
[3] Brain & Spine Inst, Paris, France
[4] Erasmus Univ, Rotterdam, Netherlands
关键词
consensus PCA; hierarchical PCA; MAXBET; MAXDIFF; MAXVAR; multiblock component methods; PLS path modeling; GCCA; RGCCA; SSQCOR; SUMCOR; PLS; VARIABLES; SETS; MODELS;
D O I
10.1007/s11336-017-9573-x
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
A new framework for sequential multiblock component methods is presented. This framework relies on a new version of regularized generalized canonical correlation analysis (RGCCA) where various scheme functions and shrinkage constants are considered. Two types of between block connections are considered: blocks are either fully connected or connected to the superblock (concatenation of all blocks). The proposed iterative algorithm is monotone convergent and guarantees obtaining at convergence a stationary point of RGCCA. In some cases, the solution of RGCCA is the first eigenvalue/eigenvector of a certain matrix. For the scheme functions x, , or and shrinkage constants 0 or 1, many multiblock component methods are recovered.
引用
收藏
页码:737 / 777
页数:41
相关论文
共 50 条
  • [41] Generalized canonical correlation analysis for labeled data
    Sakamoto, Kenta
    Okabe, Masaaki
    Yadoshisa, Hiroshi
    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS (KSE 2021), 2021, 192 : 517 - 525
  • [42] Robust methods for canonical correlation analysis
    Dehon, C
    Filzmoser, P
    Croux, C
    DATA ANALYSIS, CLASSIFICATION, AND RELATED METHODS, 2000, : 321 - 326
  • [43] Sparse Generalized Canonical Correlation Analysis (DSGCCA)
    Guo, Chenfeng
    Wu, Dongrui
    2019 CHINESE AUTOMATION CONGRESS (CAC2019), 2019, : 1959 - 1964
  • [44] Variable selection for generalized canonical correlation analysis
    Tenenhaus, Arthur
    Philippe, Cathy
    Guillemot, Vincent
    Le Cao, Kim-Anh
    Grill, Jacques
    Frouin, Vincent
    BIOSTATISTICS, 2014, 15 (03) : 569 - 583
  • [45] Generalized Canonical Correlation Analysis Using GSVD
    Liu, Fu-Chang
    Sun, Quan-Sen
    Zhang, Jian
    Xia, De-Shen
    ISCSCT 2008: INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND COMPUTATIONAL TECHNOLOGY, VOL 1, PROCEEDINGS, 2008, : 136 - 141
  • [46] Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging
    Carlos Sevilla-Salcedo
    Vanessa Gómez-Verdejo
    Jussi Tohka
    Neuroinformatics, 2020, 18 : 641 - 659
  • [47] Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging
    Sevilla-Salcedo, Carlos
    Gomez-Verdejo, Vanessa
    Tohka, Jussi
    NEUROINFORMATICS, 2020, 18 (04) : 641 - 659
  • [48] Sequential Error Concealment via Canonical Correlation Analysis
    Fan, Wen
    Liang, Junli
    Ye, Xin
    Li, Min
    2015 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING (ICSPCC), 2015, : 844 - 847
  • [49] A generalized framework for Network Component Analysis
    Boscolo, R
    Sabatti, C
    Liao, JC
    Roychowdhury, VP
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2005, 2 (04) : 289 - 301
  • [50] Enhancing reproducibility of fMRI statistical maps using generalized canonical correlation analysis in NPAIRS framework
    Afshin-Pour, Babak
    Hossein-Zadeh, Gholam-Ali
    Strother, Stephen C.
    Soltanian-Zadeh, Hamid
    NEUROIMAGE, 2012, 60 (04) : 1970 - 1981