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Metric learning: A general dimension reduction framework for classification and visualization

Lu, C, Feng, G, Jiang, J and Wang, P (2008) Metric learning: A general dimension reduction framework for classification and visualization In: ICPR 2008, 2008-12-08 - 2008-12-11, Tampa, USA.

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Abstract

A new general dimension reduction framework based on similar and dissimilar metric learning is proposed in this paper which allows us to exploit the geometry of data to reduce the data dimension for classification and visualization. The general formulation can unify the existing dimension reduction algorithms within a common framework. Furthermore, this metric learning framework can be used as a general platform for developing new dimension reduction algorithms. By utilizing this framework as a tool, we propose a novel supervised dimension reduction algorithm named sub-manifold preserving analysis (SMPA) in which the intrinsic sub-manifold structure will be preserved while the margin of interclass will be separated. Experimental evidences show that performance of our proposed SMPA algorithm is better than other algorithms.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Lu, CUNSPECIFIEDUNSPECIFIED
Feng, GUNSPECIFIEDUNSPECIFIED
Jiang, Jjianmin.jiang@surrey.ac.ukUNSPECIFIED
Wang, PUNSPECIFIEDUNSPECIFIED
Date : 2008
Identification Number : https://doi.org/10.1109/ICPR.2008.4761130
Contributors :
ContributionNameEmailORCID
publisherIEEE, UNSPECIFIEDUNSPECIFIED
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:25
Last Modified : 17 May 2017 15:03
URI: http://epubs.surrey.ac.uk/id/eprint/835270

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