Name
Abstract | ||
|---|---|---|
A novel discriminant analysis technique for feature extraction, referred to as Robust Discriminant Subspace (RDS) with L2,p+s-Norm Distance Maximization-Minimization (maxmin) is posed. In its objective, the within-class and between-class distances are measured by L2,p-norm and L2,s-norm, respectively, such that it is robust and rotational invariant. An efficient iterative algorithm is designed to solve the resulted objective, which is non-greedy. We also conduct some insightful analysis on the convergence of the proposed algorithm. Theoretical insights and effectiveness of our RDS are further supported by promising experimental results on several images databases. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICPR.2018.8545100 | 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
Keywords | Field | DocType |
robust discriminant analysis, L2,p-norm distance maximization, L2,s-norm minimization | Pattern recognition,Subspace topology,Discriminant,Computer science,Iterative method,Feature extraction,Robustness (computer science),Invariant (mathematics),Artificial intelligence,Linear discriminant analysis,Contextual image classification | Conference |
ISSN | Citations | PageRank |
1051-4651 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiaolin Ye | 1 | 397 | 27.02 |
| Zhao Zhang | 2 | 938 | 65.99 |