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On dimensionality reduction for classification and its application (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Raich, Raviv Costa, Jose A. |
| Description | IEEE International Conference on Acoustics, Speech, and Signal Processing |
| Abstract | In this paper, we evaluate the contribution of the classification constrained dimensionality reduction (CCDR) algorithm to the performance of several classifiers. We present an extension to previously introduced CCDR algorithm to multiple hypotheses. We investigate classification performance using the CCDR algorithm on hyperspectral satellite imagery data. We demonstrate the performance gain for both local and global classifiers and demonstrate a 10 % improvement of the k-nearest neighbors algorithm performance. We present a connection between intrinsic dimension estimation and the optimal embedding dimension obtained using the CCDR algorithm. 1. |
| File Format | |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | Dimensionality Reduction Classification Performance Global Classifier Intrinsic Dimension Estimation Performance Gain Ccdr Algorithm Hyperspectral Satellite Imagery Data K-nearest Neighbor Algorithm Performance Several Classifier |
| Content Type | Text |
| Resource Type | Conference Proceedings |
