Name
Abstract | ||
|---|---|---|
Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher's linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c - 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TPAMI.2008.70 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
subspace selection,projected subspace,linear dimensionality reduction step,linear discriminant analysis,kl divergence,subspace selection approach,geometric mean,mean value,potential discriminative subspace selection,important subspace approach,indexing terms,synthetic data,arithmetic mean,machine learning,kullback leibler divergence,probability and statistics,data visualization,feature space,bioinformatics,merging,information analysis,visualization,information management,gaussian distribution,geometry,numerical analysis,biometrics,feature extraction,covariance matrix,data reduction,kullback leibler | Feature vector,Dimensionality reduction,Subspace topology,Pattern recognition,Feature extraction,Artificial intelligence,Covariance matrix,Linear discriminant analysis,Kullback–Leibler divergence,Mathematics,Covariance | Journal |
Volume | Issue | ISSN |
31 | 2 | 0162-8828 |
Citations | PageRank | References |
264 | 7.05 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dacheng Tao | 1 | 19032 | 747.78 |
| Xuelong Li | 2 | 15049 | 617.31 |
| Xindong Wu | 3 | 8830 | 503.63 |
| Stephen J. Maybank | 4 | 4105 | 493.12 |