L1-PCA signal subspace identification for non-sphered data under the ICA model

Abstract : Principal component analysis (PCA) is an ubiquitous data compression and feature extraction technique in signal processing and machine learning. As compared with the classical L2-norm PCA, its L1-norm version offers increased robustness to outliers that are usually present in faulty data. Recently, L1- PCA was shown to perform source recovery when the observed data follow an independent component analysis (ICA) model. However, proof of this result requires the data to be sphered, i.e., to be preprocessed to constrain their covariance matrix to be the identity. The present contribution extends this result by relaxing the sphering assumption and allowing the data to have arbitrary covariance matrix. We prove that L1-PCA is indeed able to identify the mixing matrix columns associated with the strongest independent sources, thus performing signal subspace identification with improved robustness to outliers. Numerical experiments illustrate and confirm the theoretical findings.
Type de document :
Communication dans un congrès
Proc. CAMSAP-2017, 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Dec 2017, Curaçao, Netherlands Antilles. Proc. CAMSAP-2017, 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01719134
Contributeur : Vicente Zarzoso <>
Soumis le : vendredi 1 février 2019 - 11:13:08
Dernière modification le : mercredi 13 février 2019 - 14:38:15

Fichier

camsap17mc.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01719134, version 1

Collections

Citation

Rubén Martín-Clemente, Vicente Zarzoso. L1-PCA signal subspace identification for non-sphered data under the ICA model. Proc. CAMSAP-2017, 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Dec 2017, Curaçao, Netherlands Antilles. Proc. CAMSAP-2017, 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. 〈hal-01719134〉

Partager

Métriques

Consultations de la notice

55

Téléchargements de fichiers

4