A robust maximum correntropy criterion for dictionary learning

Carlos A. Loza, Jose C. Principe

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10 Citas (Scopus)

Resumen

We introduce a method that incorporates robustness to one of the main building blocks of sparse modeling: dictionary learning. Particularly, we exploit correntropy to compute the principal components in cases where outliers might be detrimental without proper care. This is further added to one of the most utilized dictionary learning tools: K-SVD; the result is Correntropy K-SVD, or CK-SVD, a method that is based on a Maximum Correntropy Criterion (MCC) instead of the somewhat limited Minimum Squared Error (MSE) approach. The optimization is performed using the well-known Half-Quadratic (HQ) technique, which allows a fast and efficient implementation. The results show the importance of this work not only by outperforming K-SVD, but also by circumventing one of the main assumptions during learning overcomplete representations: the availability of untampered, noiseless and outlier-free samples for training stages.

Idioma originalInglés
Título de la publicación alojada2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
EditoresKostas Diamantaras, Aurelio Uncini, Francesco A. N. Palmieri, Jan Larsen
EditorialIEEE Computer Society
ISBN (versión digital)9781509007462
DOI
EstadoPublicada - 8 nov. 2016
Publicado de forma externa
Evento26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italia
Duración: 13 sep. 201616 sep. 2016

Serie de la publicación

NombreIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volumen2016-November
ISSN (versión impresa)2161-0363
ISSN (versión digital)2161-0371

Conferencia

Conferencia26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
País/TerritorioItalia
CiudadVietri sul Mare, Salerno
Período13/09/1616/09/16

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