Exact Recovery of Sparse-Used Dictionaries
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 3087-3089.
Abstract
We consider the problem of learning sparsely used dictionaries with an arbitrary square dictionary and a random, sparse coefficient matrix. We prove that O (n log n) 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.
@inproceedings{ijcai2013p462,
title = {Exact Recovery of Sparse-Used Dictionaries},
author = {Wang, Huan and Spielman, Daniel A. and Wright, John},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {3087--3089},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/462},
}
TY - CPAPER TI - Exact Recovery of Sparse-Used Dictionaries AU - Wang, Huan AU - Spielman, Daniel A. AU - Wright, John T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 3087 EP - 3089 UR - https://ijcai.org/proceedings/2013/462 ER -Copied
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