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Feature selection based on maximizing separability in Gauss mixture model and its application to image classification

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2 Author(s)
Sangho Yoon ; Lab. of Inf. Syst., Stanford Univ., CA, USA ; R. M. Gray

We propose a feature selection algorithm suitable for classification problems. Our algorithm tries to find a subset of features, which maximizes separability between Gaussian clusters. To reduce the complexity of exhaustive searching the best feature set, we follow a backward elimination method. Our feature selection algorithm can be applied to a full search classifier to obtain a single global subspace. However, one global subspace may not alone capture local behavior well. We realize multiple subspace clustering by applying our dimension reduction algorithm to a tree structured classifier. Experimental results show that the resulting classifier not only removes irrelevant features but also improves classification performance.

Published in:

IEEE International Conference on Image Processing 2005  (Volume:2 )

Date of Conference:

11-14 Sept. 2005