NDLI: A new kernel clustering algorithm

Content Provider	IEEE Xplore Digital Library
Author	Borer, S. Gerstner, W.
Copyright Year	2002
Description	Author affiliation: Lab. of Computational Neurosci., Swiss Fed. Inst. of Technol., Lausanne, Switzerland (Borer, S.; Gerstner, W.)
Abstract	We propose a new kernel clustering algorithm. It estimates an in advance fixed number of vectors and margins in a feature space. Each pair of vector and margin defines a hyperplane in feature space and thus separates the data in two clusters. All the clusters together carry important information about the data set. The estimation in feature space is done implicitly by the use of a kernel. Therefore nonlinear clusters in the space of the data can be obtained. The clusters are estimated by optimizing a homogeneous quadratic program. We show how our algorithm can be efficiently implemented and we demonstrate the usefulness with a real world example.
Sponsorship	Asia-Pacific Neural Network Assembly Singapore Neuroscience Assoc. SEAL & FSKD Conference Steering Committees IEEE Neural Networks Soc. Int. Neural Network Soc. Eur. Neural Network Soc. SPIE
Starting Page	2527
Ending Page	2531
File Size	439794
Page Count	5
File Format	PDF
ISBN	9810475241
DOI	10.1109/ICONIP.2002.1201950
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological University
Subject Keyword	Kernel Clustering algorithms Space technology Vectors Cost function Unsupervised learning Data mining Laboratories Independent component analysis Principal component analysis
Content Type	Text
Resource Type	Article

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