Name
Abstract | ||
|---|---|---|
The existing margin-based discriminant analysis methods, which use K-nearest neighbor technique to characterize the margin, such as nonparametric discriminant analysis (NDA). These methods encounter a common problem, that is, the nearest neighbor parameter K must be chosen in advance. How to choose an optimal K is a theoretically difficult problem. In this paper, we present a new marginal characterization method using the sparse representation, which can successfully avoid the difficulty of the parameter selection. The effectiveness of the proposed method is evaluated through the experiments on AR and Extended Yale B database, and the experimental results show the fact that the performance of the proposed method superiors to the state-of-the-art feature extraction methods. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5650211 | 2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING |
Keywords | Field | DocType |
feature extraction, discriminant analysis, sparse, margin, face recognition | Optimal discriminant analysis,k-nearest neighbors algorithm,Facial recognition system,Pattern recognition,Computer science,Sparse approximation,Feature extraction,Artificial intelligence,Linear discriminant analysis,Principal component analysis,Sparse matrix | Conference |
ISSN | Citations | PageRank |
1522-4880 | 1 | 0.36 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenghong Gu | 1 | 42 | 3.14 |
| Jian Yang | 2 | 5 | 1.22 |