Name
Abstract | ||
|---|---|---|
•A robust latent representation learning framework is proposed to make robust linear regression.•The well-defined latent representation learning and robust capped lp norm regularized regression are proposed.•A discriminative linear regression method is proposed to enlarge the margins of different classes.•The theoretical and experimental analyses are presented to show the efficacy of our method.•The proposed method can achieve competitive classification results in comparison with state-of-the-arts. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patrec.2018.04.016 | Pattern Recognition Letters |
Keywords | Field | DocType |
Discriminative representation,Linear regression,Sparse representation,Latent structure,Image recognition | Pattern recognition,Feature selection,Regression,Regression analysis,Robust regression,Artificial intelligence,Discriminative model,Optimization problem,Mathematics,Feature learning,Linear regression | Journal |
Volume | ISSN | Citations |
117 | 0167-8655 | 1 |
PageRank | References | Authors |
0.34 | 32 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| jinrong cui | 1 | 63 | 3.51 |
| Qi Zhu | 2 | 147 | 11.68 |
| Ding Wang | 3 | 7 | 3.19 |
| Zuoyong Li | 4 | 348 | 27.55 |