A unifying framework for spectral analysis based dimensionality reduction

Zhang, T. and Tao, D. and Li, Xuelong and Yang, T. (2008) A unifying framework for spectral analysis based dimensionality reduction. In: UNSPECIFIED (ed.) International Joint Conference on Neural Networks. New York, USA: Institute of Electrical and Electronics Engineers, pp. 1670-1677. ISBN 9781424418206.

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Official URL: http://dx.doi.org/10.1109/IJCNN.2008.4634022

Abstract

Past decades, numerous spectral analysis based algorithms have been proposed for dimensionality reduction, which plays an important role in machine learning and artificial intelligence. However, most of these existing algorithms are developed intuitively and pragmatically, i.e., on the base of the experience and knowledge of experts for their own purposes. Therefore, it will be more informative to provide some a systematic framework for understanding the common properties and intrinsic differences in the algorithms. In this paper, we propose such a framework, i.e., ldquopatch alignmentrdquo, which consists of two stages: part optimization and whole alignment. With the proposed framework, various algorithms including the conventional linear algorithms and the manifold learning algorithms are reformulated into a unified form, which gives us some new understandings on these algorithms.

Metadata

Item Type:	Book Section
School:	Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
Depositing User:	Sarah Hall
Date Deposited:	12 Jul 2013 13:39
Last Modified:	11 Oct 2016 15:27
URI:	http://eprints.bbk.ac.uk/id/eprint/7678

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