Clustering via kernel decomposition

Szymkowiak-Have, A., Girolami, M. and Larsen, J. (2006) Clustering via kernel decomposition. IEEE Transactions on Neural Networks, 17(1), pp. 256-264. (doi: 10.1109/TNN.2005.860840)

Abstract

Spectral clustering methods were proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this letter, the affinity matrix is created from the elements of a nonparametric density estimator and then decomposed to obtain posterior probabilities of class membership. Hyperparameters are selected using standard cross-validation methods.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Girolami, Prof Mark
• Authors: Szymkowiak-Have, A., Girolami, M., and Larsen, J.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: IEEE Transactions on Neural Networks
• Publisher: IEEE
• ISSN: 1045-9227
• Copyright Holders: Copyright © 2006 IEEE
• First Published: First published in IEEE Transactions on Neural Networks 17(1):256-264
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher.