Name
Abstract | ||
|---|---|---|
This paper presents a novel algorithm - robust kernel principal component analysis (robust KPCA), on the basis of the research of kernel principal component analysis (KPCA) and robust principal component analysis (RPCA). First, this algorithm sets the radius of the images of the training samples in the feature space using kernel tricks, then determines whether the samples are outliers or not, and finally analyzes the training samples which have eliminated the outliers using KPCA algorithm. The improved KPCA algorithm not only retains the non-linearity property of KPCA algorithm but also gets better robustness. Because the effects of outliers are eliminated, robust KPCA algorithm gets higher accuracy than KPCA algorithm for data analysis. The simulation experiments show that the robust KPCA algorithm developed is better than the KPCA algorithm. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/ICMLC.2004.1378562 | ICASSP (6) |
Keywords | Field | DocType |
robust kernel pca algorithm,kernel tricks,image samples,nonlinear function,image feature space,training sample analysis,data analysis,outlier effect elimination,image sampling,feature extraction,nonlinear functions,principal component analysis,signal reconstruction,simulation experiment,kernel pca,kernel principal component analysis,computational modeling,adaptive signal processing,statistical analysis,feature space,iterative methods,gaussian distribution,kernel,robustness | Feature vector,Pattern recognition,Iterative method,Computer science,Outlier,Algorithm,Robustness (computer science),Kernel principal component analysis,Adaptive filter,Artificial intelligence,Principal component analysis,Signal reconstruction | Conference |
Volume | Issue | ISBN |
5 | null | 0-7803-8403-2 |
Citations | PageRank | References |
6 | 0.66 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Congde Lu | 1 | 14 | 2.72 |
| Taiyi Zhang | 2 | 176 | 17.60 |
| Ruonan Zhang | 3 | 262 | 41.02 |
| Chunmei Zhang | 4 | 6 | 0.66 |