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.


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    Title :

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


    Contributors:
    Sangho Yoon, (author) / Gray, R.M. (author)


    Publication date :

    2005-01-01


    Size :

    272373 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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