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.
Feature selection based on maximizing separability in Gauss mixture model and its application to image classification
IEEE International Conference on Image Processing 2005 ; 2 ; II-1198
2005-01-01
272373 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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