Name
Abstract | ||
|---|---|---|
We mainly propose a robust Embedded Locality-Constrained Label Consistent Dictionary Learning (ELC2DL) framework for discriminative classification. ELC2DL improves the representation and classification performance by performing DL in the noise-removed sparse embedding space, since most real data often contains noise and performing DL over noisy data for reconstruction may decrease performance potentially. To reduce the noise in data, our model computes a sparse projection jointly for noise reduction and then uses the noise-removed data for DL. By incorporating a noise-reduction term with a discriminative locality-constrained label consistent term that associates the label information with each dictionary atom to preserve local structure of training data, a noise-reduction projection, an over-complete dictionary and discriminative sparse codes are obtained jointly. Simulations on several image databases show that our algorithm can deliver enhanced performance over other state-of-the-arts. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICPR.2018.8545446 | 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
Keywords | Field | DocType |
Robust locality-constrained label consistent KSVD, joint sparse embedding, dictionary learning, classification | Training set,Noise reduction,Locality,Embedding,K-SVD,Pattern recognition,Computer science,Artificial intelligence,Discriminative model,Sparse matrix,Encoding (memory) | Conference |
ISSN | Citations | PageRank |
1051-4651 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhao Zhang | 1 | 938 | 65.99 |
| Weiming Jiang | 2 | 100 | 8.50 |
| Sheng Li | 3 | 609 | 53.39 |
| Jie Qin | 4 | 167 | 17.38 |
| Guangcan Liu | 5 | 2515 | 76.85 |
| Shuicheng Yan | 6 | 9701 | 359.54 |