Feature Clustering based MIM for a New Feature Extraction Method
Keywords:feature extraction, Mutual Information Maximization (MIM), similarity measure, clustering
In this paper, a new unsupervised Feature Extraction appoach is presented,Â which is based on feature clustering algorithm. Applying a divisive clusteringÂ algorithm, the method search for a compression of the information contained in theÂ original set of features. It investigates the use of Mutual Information MaximizationÂ (MIM) to find appropriate transformation of clusterde features. Experiments on UCIÂ datasets show that the proposed method often outperforms conventional unsupervisedÂ methods PCA and ICA from the point of view of classification accuracy.
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