Sparse and smooth canonical correlation analysis through rank-1 matrix approximation - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue EURASIP Journal on Advances in Signal Processing Année : 2017

Sparse and smooth canonical correlation analysis through rank-1 matrix approximation

Résumé

Canonical correlation analysis (CCA) is a well-known technique used to characterize the relationship between two sets of multidimensional variables by finding linear combinations of variables with maximal correlation. Sparse CCA and smooth or regularized CCA are two widely used variants of CCA because of the improved interpretability of the former and the better performance of the later. So far, the cross-matrix product of the two sets of multidimensional variables has been widely used for the derivation of these variants. In this paper, two new algorithms for sparse CCA and smooth CCA are proposed. These algorithms differ from the existing ones in their derivation which is based on penalized rank-1 matrix approximation and the orthogonal projectors onto the space spanned by the two sets of multidimensional variables instead of the simple cross-matrix product. The performance and effectiveness of the proposed algorithms are tested on simulated experiments. On these results, it can be observed that they outperform the state of the art sparse CCA algorithms.
Fichier principal
Vignette du fichier
art3A10.11862Fs13634-017-0459-y.pdf (2.37 Mo) Télécharger le fichier
Origine : Publication financée par une institution
Loading...

Dates et versions

hal-01548497 , version 1 (27-06-2017)

Identifiants

Citer

Abdeldjalil Aissa El Bey, Abd-Krim Seghouane. Sparse and smooth canonical correlation analysis through rank-1 matrix approximation. EURASIP Journal on Advances in Signal Processing, 2017, 2017, pp.Art. 25 - ⟨10.1186/s13634-017-0459-y⟩. ⟨hal-01548497⟩
121 Consultations
161 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More