Clustering via Hilbert space

David Horn

Physica A: Statistical Mechanics and its Applications, 2001, vol. 302, issue 1, 70-79

Abstract: We discuss novel clustering methods that are based on mapping data points to a Hilbert space by means of a Gaussian kernel. The first method, support vector clustering (SVC), searches for the smallest sphere enclosing data images in Hilbert space. The second, quantum clustering (QC), searches for the minima of a potential function defined in such a Hilbert space.

Keywords: Clustering; Support vector clustering; Hilbert space; Kernel methods; Scale-space clustering; Schrödinger equation (search for similar items in EconPapers)
Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:302:y:2001:i:1:p:70-79

DOI: 10.1016/S0378-4371(01)00442-3

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