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Classification constrained dimensionality reduction (2005)
| Content Provider | CiteSeerX |
|---|---|
| Author | Costa, Jose A. Hero Iii, Alfred O. |
| Description | In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing In this paper, we propose a nonlinear dimensionality reduction method aimed at extracting lower-dimensional features relevant for classification tasks. This is obtained by modifying the Laplacian approach to manifold learning through the introduction of class dependent constraints. Using synthetic data sets, we show that the proposed algorithm can greatly improve both supervised and semi-supervised learning problems. 1. |
| File Format | |
| Language | English |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Nonlinear Dimensionality Reduction Method Dimensionality Reduction Lower-dimensional Feature Laplacian Approach Synthetic Data Set Classification Task Semi-supervised Learning Problem Class Dependent Constraint |
| Content Type | Text |
| Resource Type | Article |
