Classification constrained dimensionality reduction
Costa, J.A.; Hero, A.O., III
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP apos;05). IEEE International Conference on
Volume 5, Issue , 18-23 March 2005 Page(s): v/1077 - v/1080 Vol. 5
Digital Object Identifier   10.1109/ICASSP.2005.1416494
Summary: 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.

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