Cross-Entropy Clustering Approach to One-Class Classification
نویسندگان
چکیده
Cross-entropy clustering (CEC) is a density model based clustering algorithm. In this paper we present a possible application of CEC to the one-class classification, which has several advantage over classical approaches based on Expectation Maximization (EM) and Support Vector Machines (SVM). More precisely, we can use various types of gaussian models with lower computational complexity. We test the designed method on real data coming from the monitoring systems of wind turbines.
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