@inproceedings{e216af3ea24a42b2b213fa662009b913,
title = "Dictionary learning for sparse representation: Complexity and algorithms",
abstract = "In this paper we consider the dictionary learning problem for sparse representation. We first show that this problem is NP-hard and then propose an efficient dictionary learning scheme to solve several practical formulations of this problem. Unlike many existing algorithms in the literature, such as K-SVD, our proposed dictionary learning scheme is theoretically guaranteed to converge to the set of stationary points under certain mild assumptions. For the image denoising application, the performance and the efficiency of the proposed dictionary learning scheme are comparable to that of K-SVD algorithm in simulation.",
keywords = "Dictionary learning, K-SVD, computational complexity, sparse representation",
author = "Meisam Razaviyayn and Tseng, {Hung Wei} and Luo, {Zhi Quan}",
year = "2014",
month = jan,
day = "1",
doi = "10.1109/ICASSP.2014.6854604",
language = "English (US)",
isbn = "9781479928927",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5247--5251",
booktitle = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014",
note = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 ; Conference date: 04-05-2014 Through 09-05-2014",
}