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Dimensionality reduction of clustered data sets

Sanguinetti, G. (2008) Dimensionality reduction of clustered data sets. IEEE Transactions Pattern Analysis and Machine Intelligence, 30 (3). pp. 535-540. ISSN 0162-8828

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Abstract

We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised generalisation of linear discriminant analysis. This provides a completely new approach to one of the most established and widely used classification algorithms. The performance of the model is then demonstrated on a number of real and artificial data sets.

Item Type: Article
Copyright, Publisher and Additional Information: © Copyright 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Sherpa Assistant
Date Deposited: 08 Feb 2008 16:42
Last Modified: 08 Feb 2013 16:55
Published Version: http://dx.doi.org/10.1109/TPAMI.2007.70819
Status: Published
Publisher: IEEE
Refereed: Yes
Identification Number: 10.1109/TPAMI.2007.70819
URI: http://eprints.whiterose.ac.uk/id/eprint/3622

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