Name
Abstract | ||
|---|---|---|
In this paper, the problem of dictionary learning and its analogy to source separation is addressed. First, we extend the well-known method of K-SVD to incoherent K-SVD, to enforce the algorithm to achieve an incoherent dictionary. Second, a fast dictionary learning algorithm based on steepest descent method is proposed. The main advantage of this method is high speed since both coefficients and dictionary elements are updated simultaneously rather than column-by-column. Finally, we apply the proposed methods to both synthetic and real functional magnetic resonance imaging data for the detection of activated regions in the brain. The results of our experiments confirm the effectiveness of the proposed ideas. In addition, we compare the quality of results and empirically prove the superiority of the proposed dictionary learning methods over the conventional algorithms. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s11760-013-0429-2 | Signal, Image and Video Processing |
Keywords | Field | DocType |
Adaptive step size, Blind source separation, Compressed sensing, Dictionary learning, Steepest descent | Gradient descent,Dictionary learning,K-SVD,Method of steepest descent,Pattern recognition,Computer science,Algorithm,Artificial intelligence,Blind signal separation,Compressed sensing,Source separation | Journal |
Volume | Issue | ISSN |
9 | 1 | 1863-1711 |
Citations | PageRank | References |
24 | 0.94 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vahid Abolghasemi | 1 | 274 | 22.58 |
| Saideh Ferdowsi | 2 | 147 | 10.85 |
| Saeid Sanei | 3 | 530 | 72.63 |