Learning Overcomplete Dictionaries Based On Parallel Atom-Updating
Mostafa Sadeghi, Massoud Babaie-Zadeh, Christian Jutten

‎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.