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Adaptive-Size Dictionary Learning Using Information Theoretic Criteria

1
Department of Automatic Control and Computers, University Politehnica of Bucharest, 313 Spl. Independenţei, 060042 Bucharest, Romania
2
Department of Statistics, University of Auckland, Auckland 1142, New Zealand
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Author to whom correspondence should be addressed.
Algorithms 2019, 12(9), 178; https://doi.org/10.3390/a12090178
Received: 14 July 2019 / Revised: 20 August 2019 / Accepted: 22 August 2019 / Published: 25 August 2019
(This article belongs to the Special Issue Dictionary Learning Algorithms and Applications)
Finding the size of the dictionary is an open issue in dictionary learning (DL). We propose an algorithm that adapts the size during the learning process by using Information Theoretic Criteria (ITC) specialized to the DL problem. The algorithm is built on top of Approximate K-SVD (AK-SVD) and periodically removes the less used atoms or adds new random atoms, based on ITC evaluations for a small number of candidate sub-dictionaries. Numerical experiments on synthetic data show that our algorithm not only finds the true size with very good accuracy, but is also able to improve the representation error in comparison with AK-SVD knowing the true size. View Full-Text
Keywords: dictionary learning; sparse representation; information theoretic criteria; dictionary size dictionary learning; sparse representation; information theoretic criteria; dictionary size
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Dumitrescu, B.; Giurcăneanu, C.D. Adaptive-Size Dictionary Learning Using Information Theoretic Criteria. Algorithms 2019, 12, 178.

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