Name
Abstract | ||
|---|---|---|
In this paper, a novel discriminative dictionary learning approach is proposed that attempts to preserve the local structure of the data while encouraging discriminability. The reconstruction error and sparsity inducing ℓ1-penalty of dictionary learning are minimized alongside a locality preserving and discriminative term. In this setting, each data point is represented by a sparse linear combination of dictionary atoms with the goal that its k-nearest same-label neighbors are preserved. Since the class of a new data point is unknown, its sparse representation is found once for each class. The class that produces the lowest error is associated with that point. Experimental results on five common classification datasets, show that this method outperforms state-of-the-art classifiers, especially when the training data is limited. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7026061 | Image Processing |
Keywords | Field | DocType |
learning (artificial intelligence),pattern classification,classification datasets,dictionary atoms,k-nearest same-label neighbors,local data structure,locality preserving discriminative dictionary learning,reconstruction error,sparse linear combination,sparsity inducing l1-penalty,training data,Classification,discriminative dictionary learning,locality preserving,supervised learning | Linear combination,Locality,Dictionary learning,Pattern recognition,K-SVD,Computer science,Sparse approximation,Local structure,Supervised learning,Artificial intelligence,Discriminative model,Machine learning | Conference |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
19 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siavash Haghiri | 1 | 0 | 0.34 |
| Hamid R. Rabiee | 2 | 336 | 41.77 |
| Ali Soltani-Farani | 3 | 0 | 0.34 |
| Seyyed Abbas Hosseini | 4 | 62 | 3.24 |
| Maryam Shadloo | 5 | 0 | 0.34 |
| Soltani-Farani, A. | 6 | 0 | 0.34 |