Merging and splitting eigenspace models

Hall, P., Marshall, Andrew David ORCID: https://orcid.org/0000-0003-2789-1395 and Martin, Ralph Robert 2000. Merging and splitting eigenspace models. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (9) , pp. 1042-1049. 10.1109/34.877525

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Abstract

We present new deterministic methods that, given two eigenspace models-each representing a set of n-dimensional observations-will: 1) merge the models to yield a representation of the union of the sets and 2) split one model from another to represent the difference between the sets. As this is done, we accurately keep track of the mean. Here, we give a theoretical derivation of the methods, empirical results relating to the efficiency and accuracy of the techniques, and three general applications, including the construction of Gaussian mixture models that are dynamically updateable.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Additional Information:	© 2000 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Publisher:	IEEE
ISSN:	0162-8828
Date of First Compliant Deposit:	30 March 2016
Last Modified:	20 Nov 2024 03:00
URI:	https://orca.cardiff.ac.uk/id/eprint/13566

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