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[LNS19] Sparse Correspondence Analysis

Conférences invitées : ASMDA 2019, June 2019, pp.7, Florence, Italie,

Mots clés: sparse methods, correspondence analysis, canonical analysis

Résumé: Since the introduction of the lasso in regression, various sparse methods have been developped in an unsupervised context like sparsePCA which is a combination of feature selection and dimension reduction. Their interest is to simplify the interpretation of the pseudo principal components since each is expressed as a linear combination of only a small number of variables. The disadvantages lie on the one hand in the difficulty of choosing the number of non-zero coefficients in the absence of a criterion and on the other hand in the loss of orthogonality properties for the components and/or the loadings. In this paper we are interested in sparse variants of correspondence analysis (CA) for large contingency tables like documents-terms matrices. We use the fact that CA is both a PCA (or a weighted SVD) and a canonical analysis, in order to develop column sparse CA and rows and columns doubly sparse CA.

Commentaires: The 18th Conference of the Applied Stochastic Models and Data Analysis International Society

BibTeX

@inproceedings {
LNS19,
title="{Sparse Correspondence Analysis}",
author=" R. Liu and N. Niang Keita and G. Saporta and H. Wang ",
booktitle="{ASMDA 2019}",
year=2019,
month="June",
pages="7",
address="Florence, Italie",
note="{The 18th Conference of the Applied Stochastic Models and Data Analysis International Society}",
}