Learning overcomplete dictionaries based on parallel atom-updating

Abstract : In this paper we propose a fast and efficient algorithm for learning overcomplete dictionaries. The proposed algorithm is indeed an alternative to the well-known K-Singular Value Decomposition (K-SVD) algorithm. The main drawback of K-SVD is its high computational load especially in high- dimensional problems. This is due to the fact that in the dictionary update stage of this algorithm an SVD is performed to update each column of the dictionary. Our proposed algorithm avoids performing SVD and instead uses a special form of alternating minimization. In this way, as our simulations on both synthetic and real data show, our algorithm outperforms K-SVD in both computational load and the quality of the results.
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Submitted on : Friday, September 27, 2013 - 4:40:08 PM
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Mostafa Sadeghi, Massoud Babaie-Zadeh, Christian Jutten. Learning overcomplete dictionaries based on parallel atom-updating. IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2013), Sep 2013, Southampton, United Kingdom. 5 p. ⟨hal-00867170⟩



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