Fast Greedy Algorithms for Dictionary Selection with Generalized Sparsity Constraints
Abstract
In dictionary selection, several atoms are selected from finite candidates that successfully approximate given data points in the sparse representation. We propose a novel efficient greedy algorithm for dictionary selection. Not only does our algorithm work much faster than the known methods, but it can also handle more complex sparsity constraints, such as average sparsity. Using numerical experiments, we show that our algorithm outperforms the known methods for dictionary selection, achieving competitive performances with dictionary learning algorithms in a smaller running time.
Cite
Text
Fujii and Soma. "Fast Greedy Algorithms for Dictionary Selection with Generalized Sparsity Constraints." Neural Information Processing Systems, 2018.Markdown
[Fujii and Soma. "Fast Greedy Algorithms for Dictionary Selection with Generalized Sparsity Constraints." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/fujii2018neurips-fast/)BibTeX
@inproceedings{fujii2018neurips-fast,
title = {{Fast Greedy Algorithms for Dictionary Selection with Generalized Sparsity Constraints}},
author = {Fujii, Kaito and Soma, Tasuku},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {4744-4753},
url = {https://mlanthology.org/neurips/2018/fujii2018neurips-fast/}
}