Regularized Consensus PCA
Methodology
2015-04-28 v1
Abstract
A new framework for many multiblock component methods (including consensus and hierarchical PCA) is proposed. It is based on the consensus PCA model: a scheme connecting each block of variables to a superblock obtained by concatenation of all blocks. Regularized consensus PCA is obtained by applying regularized generalized canonical correlation analysis to this scheme for the function where . A gradient algorithm is proposed. At convergence, a solution of the stationary equation related to the optimization problem is obtained. For m = 1, 2 or 4 and shrinkage constants equal to 0 or 1, many multiblock component methods are recovered.
Keywords
Cite
@article{arxiv.1504.07005,
title = {Regularized Consensus PCA},
author = {Michel Tenenhaus and Arthur Tenenhaus and Patrick J. F. Groenen},
journal= {arXiv preprint arXiv:1504.07005},
year = {2015}
}