Name
Abstract | ||
|---|---|---|
This paper proposes a dictionary learning framework that combines the proposed block/group (BGSC) or reconstructed block/group (R-BGSC) sparse coding schemes with the novel Intra-block Coherence Suppression Dictionary Learning algorithm. An important and distinguishing feature of the proposed framework is that all dictionary blocks are trained simultaneously with respect to each data group while the intra-block coherence being explicitly minimized as an important objective. We provide both empirical evidence and heuristic support for this feature that can be considered as a direct consequence of incorporating both the group structure for the input data and the block structure for the dictionary in the learning process. The optimization problems for both the dictionary learning and sparse coding can be solved efficiently using block-gradient descent, and the details of the optimization algorithms are presented. We evaluate the proposed methods using well-known datasets, and favorable comparisons with state-of-the-art dictionary learning methods demonstrate the viability and validity of the proposed framework. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CVPR.2013.55 | CVPR |
Keywords | Field | DocType |
optimisation,state-of-the-art dictionary,group structure,image coding,novel intra-block coherence suppression dictionary learning algorithm,proposed framework,learning (artificial intelligence),dictionaries,intra-block coherence,dictionary learning,sparse coding,reconstructed block,hand-written digit recognition,block structure,proposed block,block group,optimization problems,r-bgsc,data group,bgsc,reconstructed block group sparse coding schemes,block-gradient descent,handwriting recognition,dictionary blocks,dictionary block,group regularized sparse modeling,linear programming,coherence,learning artificial intelligence,vectors,encoding | Heuristic,Dictionary learning,Pattern recognition,K-SVD,Computer science,Neural coding,Handwriting recognition,Coherence (physics),Artificial intelligence,Optimization algorithm,Optimization problem | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1063-6919 |
Citations | PageRank | References |
21 | 0.65 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Tseh Chi | 1 | 41 | 2.30 |
| Mohsen Ali | 2 | 38 | 8.40 |
| Ajit Rajwade | 3 | 160 | 18.32 |
| Jeffrey Ho | 4 | 2190 | 101.78 |