Robust K-SVD: A novel approach for dictionary learning

Carlos A. Loza

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

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A novel criterion to the well-known dictionary learning technique, K-SVD, is proposed. The approach exploits the L1-norm as the cost function for the dictionary update stage of K-SVD in order to provide robustness against impulsive noise and outlier input samples. The optimization algorithm successfully retrieves the first principal component of the input samples via greedy search methods and a parameter-free implementation. The final product is Robust K-SVD, a fast, reliable and intuitive algorithm. The results thoroughly detail how, under a wide range of noisy scenarios, the proposed technique outperforms K-SVD in terms of dictionary estimation and processing time. Recovery of Discrete Cosine Transform (DCT) bases and estimation of intrinsic dictionaries from noisy grayscale patches highlight the enhanced performance of Robust K-SVD and illustrate the circumvention of a misplaced assumption in sparse modeling problems: the availability of untampered, noiseless, and outlier-free input samples for training.

Idioma originalInglés
Título de la publicación alojadaProgress in Artificial Intelligence and Pattern Recognition - 6th International Workshop, IWAIPR 2018, Proceedings
EditoresYanio Hernández Heredia, Vladimir Milián Núñez, José Ruiz Shulcloper
EditorialSpringer Verlag
Número de páginas8
ISBN (versión impresa)9783030011314
EstadoPublicada - 2018
Evento6th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2018 - Havana, Cuba
Duración: 24 sep. 201826 sep. 2018

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11047 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


Conferencia6th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2018


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