Local discriminative distance metrics ensemble learning

Mu, Y; Ding, W; Tao, D

Local discriminative distance metrics ensemble learning

Mu, Y Ding, W Tao, D

Permalink

Publication Type:: Journal Article
Citation:: Pattern Recognition, 2013, 46 (8), pp. 2337 - 2349
Issue Date:: 2013-08-01

Closed Access

	Filename	Description	Size
	2012004071OK.pdf		1.17 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Mu, Y	en_US
dc.contributor.author	Ding, W	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2013-08-01	en_US
dc.identifier.citation	Pattern Recognition, 2013, 46 (8), pp. 2337 - 2349	en_US
dc.identifier.issn	0031-3203	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27492
dc.description.abstract	The ultimate goal of distance metric learning is to incorporate abundant discriminative information to keep all data samples in the same class close and those from different classes separated. Local distance metric methods can preserve discriminative information by considering the neighborhood influence. In this paper, we propose a new local discriminative distance metrics (LDDM) algorithm to learn multiple distance metrics from each training sample (a focal sample) and in the vicinity of that focal sample (focal vicinity), to optimize local compactness and local separability. Those locally learned distance metrics are used to build local classifiers which are aligned in a probabilistic framework via ensemble learning. Theoretical analysis proves the convergence rate bound, the generalization bound of the local distance metrics and the final ensemble classifier. We extensively evaluate LDDM using synthetic datasets and large benchmark UCI datasets. © 2013 Elsevier Ltd.	en_US
dc.relation.ispartof	Pattern Recognition	en_US
dc.relation.isbasedon	10.1016/j.patcog.2013.01.010	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Local discriminative distance metrics ensemble learning	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	46	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	46	en_US

Abstract:

The ultimate goal of distance metric learning is to incorporate abundant discriminative information to keep all data samples in the same class close and those from different classes separated. Local distance metric methods can preserve discriminative information by considering the neighborhood influence. In this paper, we propose a new local discriminative distance metrics (LDDM) algorithm to learn multiple distance metrics from each training sample (a focal sample) and in the vicinity of that focal sample (focal vicinity), to optimize local compactness and local separability. Those locally learned distance metrics are used to build local classifiers which are aligned in a probabilistic framework via ensemble learning. Theoretical analysis proves the convergence rate bound, the generalization bound of the local distance metrics and the final ensemble classifier. We extensively evaluate LDDM using synthetic datasets and large benchmark UCI datasets. © 2013 Elsevier Ltd.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/27492