# Fast greedy algorithms for dictionary selection with generalized sparsity constraints

arXiv: https://arxiv.org/abs/1809.02314 · HTML: https://mlsanity.com/paper/1809.02314/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Low Rank Multi-Dictionary Selection at Scale](https://mlsanity.com/paper/2406.06960/) (2024)

## Abstract (arXiv)

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.