Abstract
We propose a new algorithm for the design of overcomplete dictionaries for sparse coding that generalizes the Sparse Coding Neural Gas (SCNG) algorithm such that it is not bound to a particular approximation method for the coecients of the dictionary elements. In an application to image reconstruction, a dictionary that has been learned using this algorithm outperforms a dictionary that has been obtained from the widely-used K-SVD algorithm, an overcomplete Haar-wavelet dictionary and an overcomplete discrete cosine transformation (DCT).
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 18th European Symposium on Artificial Neural Networks |
| Editors | Michel Verleysen |
| Number of pages | 6 |
| Publisher | ESANN |
| Publication date | 04.2010 |
| Pages | 241-246 |
| ISBN (Print) | 2-930307-10-2 |
| Publication status | Published - 04.2010 |
| Event | 18th European Symposium on Artificial Neural Networks - Bruges, Belgium Duration: 28.04.2010 → 30.04.2010 |
