TY - GEN
T1 - Robust K-SVD
T2 - 6th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2018
AU - Loza, Carlos A.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Dictionary learning
KW - K-SVD
KW - Robust estimation
UR - https://www.scopus.com/pages/publications/85057242714
U2 - 10.1007/978-3-030-01132-1_21
DO - 10.1007/978-3-030-01132-1_21
M3 - Contribución a la conferencia
AN - SCOPUS:85057242714
SN - 9783030011314
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 185
EP - 192
BT - Progress in Artificial Intelligence and Pattern Recognition - 6th International Workshop, IWAIPR 2018, Proceedings
A2 - Heredia, Yanio Hernández
A2 - Núñez, Vladimir Milián
A2 - Shulcloper, José Ruiz
PB - Springer Verlag
Y2 - 24 September 2018 through 26 September 2018
ER -