Name
Title | ||
|---|---|---|
An Improved Kernel Minimum Square Error Classification Algorithm Based on $L_{2, 1}$ -Norm Regularization. |
Abstract | ||
|---|---|---|
The kernel minimum square error classification (KMSEC) algorithm has been widely used in classification problems. It shows a good performance on image data besides the following drawbacks: not sparse in the solutions and sensitive to noises. The latter drawback will result in a decrease in the recognition performance. To this end, we propose an improved (IKMSEC) by using the $L_{2,1}$ -norm regularization, which can obtain a sparse representation of nonlinear features to guarantee an efficient classification performance. The comprehensive experiments show the promising results in face recognition and image classification. |
Year | Venue | Field |
|---|---|---|
2017 | IEEE Access | Kernel (linear algebra),Facial recognition system,Nonlinear system,Pattern recognition,Computer science,Sparse approximation,Algorithm,Robustness (computer science),Regularization (mathematics),Artificial intelligence,Statistical classification,Contextual image classification |
DocType | Volume | Citations |
Journal | 5 | 0 |
PageRank | References | Authors |
0.34 | 25 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhonghua Liu | 1 | 115 | 11.12 |
| Shan Xue | 2 | 51 | 11.69 |
| Lin Zhang | 3 | 104 | 51.47 |
| Jiexin Pu | 4 | 92 | 19.85 |
| Haijun Wang | 5 | 55 | 9.23 |