Mode region detection using improved Competitive Hebbian Learning for unsupervised clustering
نویسندگان
چکیده
The goal of this paper is to propose an improved competitive Hebbian learning for mode detection using a new activation function, to overcome its sensitivity to local irregularities in pattern distribution. This method is involved with an unsupervised clustering approach divided into four processing stages. It begins by the estimation of the probability density function, followed by a competitive training neural network with Mahalanobis distance as a metric of resemblance. This stage allows detecting the local maxima of the pdf. Then, we use the new method to analyse the connectivity between the detected maxima of the pdf upon Mahalanobis distance. The so detected groups of Maxima are then used for the classification process. The proposed approach has proven, under a number of data samples, its automaticity and efficiency in the mode region detection through optimal data topology preservation, without over or misclassification, and without using any threshold or requiring any prior information neither on the number of classes nor on the structure of their distributions in the dataset. Keywords—Probability Density Function; Competitive Neural Networks; Mahalanobis Distance; Competitive Hebbian Learning; Topology preservation; Threshold
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