Name
Abstract | ||
|---|---|---|
We present a new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification. In our framework, the neighbor and class relations of data are used to construct the embedding for classification problems. The proposed algorithm learns the embedding for the submanifold of each class by solving an optimization problem. After being embedded into a low-dimensional subspace, data points of the same class maintain their intrinsic neighbor relations, whereas neighboring points of different classes no longer stick to one another. Via embedding, new test data are thus more reliably classified by the nearest neighbor rule, owing to the locally discriminating nature. We also describe two useful variants: two-dimensional LDE and kernel LDE. Comprehensive comparisons and extensive experiments on face recognition are included to demonstrate the effectiveness of our method. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/CVPR.2005.216 | CVPR (2) |
Keywords | Field | DocType |
optimisation,two-dimensional lde,face recognition,nearest neighbor rule,new test data,kernel lde,learning (artificial intelligence),pattern classification,different class,optimization problem,data point,classification problem,class relation,local discriminant embedding,manifold learning,intrinsic neighbor relation,nearest neighbor,testing,kernel,principal component analysis,training data,linear discriminant analysis,learning artificial intelligence,information science | k-nearest neighbors algorithm,Data point,Embedding,Pattern recognition,Subspace topology,Computer science,Feature (machine learning),Submanifold,Artificial intelligence,Nonlinear dimensionality reduction,Large margin nearest neighbor | Conference |
Volume | ISSN | ISBN |
2 | 1063-6919 | 0-7695-2372-2 |
Citations | PageRank | References |
332 | 13.10 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hwann-Tzong Chen | 1 | 826 | 52.13 |
| Huang-Wei Chang | 2 | 344 | 13.93 |
| Tyng-Luh Liu | 3 | 1384 | 85.56 |