Exact Recovery of Sparsely-Used Dictionaries


Daniel A. Spielman, Huan Wang, John Wright ;
Proceedings of the 25th Annual Conference on Learning Theory, PMLR 23:37.1-37.18, 2012.


We consider the problem of learning sparsely used dictionaries with an arbitrary square dictionary and a random, sparse coefficient matrix. We prove that \emphO(n log \emphn) samples are sufficient to uniquely determine the coefficient matrix. Based on this proof, we design a polynomial-time algorithm, called Exact Recovery of Sparsely-Used Dictionaries (ER-SpUD), and prove that it probably recovers the dictionary and coefficient matrix when the coefficient matrix is sufficiently sparse. Simulation results show that ER-SpUD reveals the true dictionary as well as the coefficients with probability higher than many state-of-the-art algorithms.

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