Name
Abstract | ||
|---|---|---|
Preserving projection-based methods are good to find the manifold structure embedded in data. As they use the Euclidean distance as a metric, which is sensitive to noise and outliers in data, nuclear norm-based two-dimensional locality preserving projection (NN-2DLPP) is thus proposed to improve the robustness of 2DLPP. However, NN-2DLPP does not consider the discriminant ability of data. In order to improve the discriminant ability of preserving projection methods, in this paper, we use preserving projection learning with structurally incoherence of data and propose structurally incoherent low-rank 2DLPP (SILR-2DLPP) for image classification. This approach provides a discriminative representation of preserving projection learning by recovering the distinct of different classes of the data. SILR-2DLPP searches the optimal subspace and low-rank representation simultaneously. We further extend SILR-2DLPP to a kernel case and propose kernel SILR-2DLPP (KSILR-2DLPP) to obtain a nonlinear representation. The theoretical analysis including convergence and computational complexity of SILR-2DLPP are presented. To verify the performance of SILR-2DLPP and KSILR-2DLPP, six well-known image databases were used in the experiments. The experimental results show that the proposed methods are superior to the previous preserving projection methods for image classification. IEEE |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCSVT.2018.2849757 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | Field | DocType |
Electronic mail,Feature extraction,feature extraction,Image classification,Kernel,low-rank,LPP,Principal component analysis,Robust,Robustness,structurally incoherent,Two dimensional displays | Kernel (linear algebra),Pattern recognition,Subspace topology,Computer science,Euclidean distance,Robustness (computer science),Feature extraction,Artificial intelligence,Contextual image classification,Discriminative model,Computational complexity theory | Journal |
Volume | Issue | ISSN |
29 | 6 | 10518215 |
Citations | PageRank | References |
4 | 0.38 | 33 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuwu Lu | 1 | 196 | 12.50 |
| Yuan Chun | 2 | 265 | 32.08 |
| Xuelong Li | 3 | 15049 | 617.31 |
| Zhihui Lai | 4 | 1204 | 76.03 |
| David Zhang | 5 | 7365 | 360.85 |
| Linlin Shen | 6 | 1351 | 90.25 |