Improving Dictionary Learning: Multiple Dictionary Updates and Coefficient Reuse
| Autor: | Michael Elad, Leslie N. Smith |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
K-SVD
Computer science business.industry Applied Mathematics Reuse Machine learning computer.software_genre Signal Processing Singular value decomposition Leverage (statistics) Artificial intelligence Electrical and Electronic Engineering Neural coding business computer Dictionary learning Natural language processing Sparse matrix |
| Zdroj: | IEEE Signal Processing Letters. 20:79-82 |
| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/lsp.2012.2229976 |
| Popis: | In this letter, we propose two improvements of the MOD and K-SVD dictionary learning algorithms, by modifying the two main parts of these algorithms-the dictionary update and the sparse coding stages. Our first contribution is a different dictionary-update stage that aims at finding both the dictionary and the representations while keeping the supports intact. The second contribution suggests to leverage the known representations from the previous sparse-coding in the quest for the updated representations. We demonstrate these two ideas in practice and show how they lead to faster training and better quality outcome. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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