Name
Abstract | ||
|---|---|---|
Recently, low-rank and sparse representation-based methods have achieved great success in subspace clustering, which aims to cluster data lying in a union of subspaces. However, most methods fail if the data samples are corrupted by noise and outliers. To solve this problem, we propose a novel robust method that uses the F-norm for dealing with universal noise and the $$l_1$$ norm or the $$l_{2,1}$$ norm for capturing outliers. The proposed method can find a low-dimensional latent space and a low-rank and sparse representation simultaneously. To preserve the local manifold structure of the data, we have adopted a graph constraint in our model to obtain a discriminative latent space. Extensive experiments on several face benchmark datasets show that our proposed method performs better than state-of-the-art subspace clustering methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00521-019-04317-3 | Neural Computing and Applications |
Keywords | DocType | Volume |
Dimension reduction, Low-rank and sparse representation, Subspace clustering, Manifold clustering | Journal | 32 |
Issue | ISSN | Citations |
12 | 0941-0643 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunjun Xiao | 1 | 0 | 0.34 |
| jia wei | 2 | 4 | 3.09 |
| Jiabing Wang | 3 | 65 | 9.20 |
| Qianli Ma | 4 | 20 | 5.80 |
| Shandian Zhe | 5 | 50 | 18.41 |
| Tolga Tasdizen | 6 | 1214 | 93.94 |