Name
Abstract | ||
|---|---|---|
This work considers the problem of learning an incoherent dictionary that is both adapted to a set of training data and incoherent so that existing sparse approximation algorithms can recover the sparsest representation. A new decorrelation method is presented that computes a fixed coherence dictionary close to a given dictionary. That step iterates pairwise decorrelations of atoms in the dictionary. Dictionary learning is then performed by adding this decorrelation method as an intermediate step in the K-SVD learning algorithm. The proposed algorithm INK-SVD is tested on musical data and compared to another existing decorrelation method. INK-SVD can compute a dictionary that approximates the training data as well as K-SVD while decreasing the coherence from 0.6 to 0.2. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICASSP.2012.6288688 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
decorrelation,dictionaries,iterative methods,signal representation,singular value decomposition,INK-SVD,K-SVD learning algorithm,decorrelation method,fixed coherence dictionary,learning incoherent dictionary,pairwise decorrelation,sparse approximation algorithm,sparse representation recovery,step iteration,Coherence,Dictionary learning,K-SVD,Sparse coding | Singular value decomposition,Approximation algorithm,Decorrelation,K-SVD,Pattern recognition,Neural coding,Computer science,Iterative method,Sparse approximation,Coherence (physics),Artificial intelligence | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4673-0044-5 | 978-1-4673-0044-5 | 24 |
PageRank | References | Authors |
0.97 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boris Mailhé | 1 | 103 | 7.22 |
| Daniele Barchiesi | 2 | 25 | 1.36 |
| M. D. Plumbley | 3 | 1915 | 202.38 |