| Paper: |
SPTM-P4.6 |
| Session: |
Adaptive Filtering and Nonlinear Systems |
| Location: |
Poster Area G |
| Session Time: |
Wednesday, March 28, 14:00 - 16:00 |
| Presentation Time: |
Wednesday, March 28, 14:00 - 16:00 |
| Presentation: |
Poster
|
| Topic: |
|
| Paper Title: |
INK-SVD: LEARNING INCOHERENT DICTIONARIES FOR SPARSE REPRESENTATIONS
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| Authors: |
Boris Mailhé, Daniele Barchiesi, Mark D. Plumbley, Queen Mary University of London, United Kingdom |
| 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. |