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Mutual information for improving the efficiency of the SCH algorithm

A new approach for improving the efficiency of a one-class classification algorithm making it more suitable for big datasets is presented in this work. The original algorithm, called SCH algorithm, approximates a Ddimensional convex hull decision by means of random projections and an ensemble of 2-dimensional decisions. With this new approach we try to get rid of the less relevant projections that lead to bad classification models in the low dimensional space. For that, after the training phase, a new stage based on mutual information is added to the original algorithm in order to remove the unnecessary projections, maintaining the information contained in the model (and thus the accuracy) while making it lightweight. This reduces remarkably the computational time of the testing phase. Finally, some experimental results demonstrate the effectiveness and efficiency of these approach.