Name
Abstract | ||
|---|---|---|
Low-rank representation (LRR) is a useful tool for seeking the lowest rank representation among all the coefficient matrices that represent the images as linear combinations of the basis in the given dictionary. However, it is an unsupervised method and has poor applicability and performance in real scenarios because of the lack of image information. In this paper, based on LRR, we propose a novel semi-supervised approach, called label constrained sparse low-rank representation (LCSLRR), which incorporates the label information as an additional hard constraint. Specifically, this paper develops an optimization process in which the improvement of the discriminating power of the low-rank decomposition is presented explicitly by adding the label information constraint. We construct LCSLRR-graph to represent data structures for semi-supervised learning and provide the weights of edges in the graph by seeking a low-rank and sparse matrix. We conduct extensive experiments on publicly available databases to verify the effectiveness of our novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11760-016-0895-4 | Signal, Image and Video Processing |
Keywords | Field | DocType |
Low-rank representation, Image classification, Semi-supervised learning, Label constraint | Linear combination,Graph,Data structure,Semi-supervised learning,Pattern recognition,Matrix (mathematics),Artificial intelligence,Contextual image classification,Machine learning,Mathematics,Sparse matrix | Journal |
Volume | Issue | ISSN |
11 | 1 | 1863-1711 |
Citations | PageRank | References |
4 | 0.39 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenxue Yang | 1 | 4 | 0.73 |
| Mao Ye | 2 | 442 | 48.46 |
| Song Tang | 3 | 17 | 2.67 |
| Tao Xiang | 4 | 9 | 1.13 |
| Zijian Liu | 5 | 4 | 0.39 |