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Multitask Principal Component Analysis

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Date
2016
Link to item file
http://jmlr.csail.mit.edu/proceedings/papers/v63/yamane65.html
Dewey
Intelligence artificielle
Sujet
dimensionality reduction; multitask learning; Riemannian geometry; Grass- mann manifold
Conference name
8th Asian Conference on Machine Learning (ACML 2016)
Conference date
11-2016
Conference city
Hamilton
Conference country
New Zealand
Book title
Proceedings of The 8th Asian Conference on Machine Learning
Author
Durrant, Robert J.; Kim, Kee-Eung
Publisher
JMLR: Workshop and Conference Proceedings
Year
2016
Pages number
460
URI
https://basepub.dauphine.fr/handle/123456789/16212
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Yamane, Ikko
92160 University of Tokyo, Graduate School of Frontier Sciences
Yger, Florian
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Berar, Maxime
23832 Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes [LITIS]
Sugiyama, Masashi
92160 University of Tokyo, Graduate School of Frontier Sciences
Type
Communication / Conférence
Item number of pages
302-317
Abstract (EN)
Principal Component Analysis (PCA) is a canonical and well-studied tool for dimension- ality reduction. However, when few data are available, the poor quality of the covariance estimator at its core may compromise its performance. We leverage this issue by casting the PCA into a multitask framework, and doing so, we show how to solve simultaneously several related PCA problems. Hence, we propose a novel formulation of the PCA prob- lem relying on a novel regularization. This regularization is based on a distance between subspaces, and the whole problem is solved as an optimization problem over a Riemannian manifold. We experimentally demonstrate the usefulness of our approach as pre-processing for EEG signals.

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