Title :
Classification constrained dimensionality reduction
Author :
Costa, Jose A. ; Hero, Alfred O., III
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA
Abstract :
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.
Keywords :
computational geometry; feature extraction; learning (artificial intelligence); signal classification; Laplacian manifold learning; class dependent constraints; classification constrained dimensionality reduction; lower-dimensional feature extraction; nonlinear dimensionality reduction; semi-supervised learning; supervised learning; Data mining; Feature extraction; Laplace equations; Machine learning; Machine learning algorithms; Manifolds; Sampling methods; Semisupervised learning; Signal processing algorithms; Symmetric matrices;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
Print_ISBN :
0-7803-8874-7
DOI :
10.1109/ICASSP.2005.1416494
