Clustering via Hilbert space

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.

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Year of publication:	2001
Authors:	Horn, David
Published in:	Physica A: Statistical Mechanics and its Applications. - Elsevier, ISSN 0378-4371. - Vol. 302.2001, 1, p. 70-79
Publisher:	Elsevier
Subject:	Clustering \| Support vector clustering \| Hilbert space \| Kernel methods \| Scale-space clustering \| Schrödinger equation

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011064367