nips nips2001 nips2001-185 nips2001-185-reference knowledge-graph by maker-knowledge-mining
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Author: David Horn, Assaf Gottlieb
Abstract: We propose a novel clustering method that is an extension of ideas inherent to scale-space clustering and support-vector clustering. Like the latter, it associates every data point with a vector in Hilbert space, and like the former it puts emphasis on their total sum, that is equal to the scalespace probability function. The novelty of our approach is the study of an operator in Hilbert space, represented by the Schr¨ dinger equation of o which the probability function is a solution. This Schr¨ dinger equation o contains a potential function that can be derived analytically from the probability function. We associate minima of the potential with cluster centers. The method has one variable parameter, the scale of its Gaussian kernel. We demonstrate its applicability on known data sets. By limiting the evaluation of the Schr¨ dinger potential to the locations of data points, o we can apply this method to problems in high dimensions.
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