Augmented embedding of dissimilarity data into (pseudo-)Euclidean spaces

Harol, A; Pekalska, E.M.; Verzakov, S; Duin, Bob

Augmented embedding of dissimilarity data into (pseudo-)Euclidean spaces

Conference paper (2006)

Authors

A Harol

E.M. Pekalska

S Verzakov

Bob Duin

To reference this document use:

http://resolver.tudelft.nl/uuid:3f240841-f913-4035-974e-7dacf5e4d509

More Info

expand_more

Published Date

2006

Language

Undefined/Unknown

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Pairwiseproximitiesdescribethepropertiesofobjectsintermsoftheirsimilarities.Byusingdi¿erentdistance-basedfunctionsonemayencodedi¿erentcharacteristicsofagivenproblem.However,tousetheframeworkofstatisticalpatternrecognitionsomevectorrepresentationshouldbeconstructed.Oneofthesimplestwaystodothatistode¿neanisometricembeddingtosomevectorspace.Inthiswork,wewillfocusonalinearembeddingintoa(pseudo-)Euclideanspace.
Thisisusuallywellde¿nedfortrainingdata.Someinadequacy,however,appearswhenprojectingnewortestobjectsduetotheresultingprojectionerrors.Inthispaperweproposeanaugmentedembeddingalgorithmthatenlargesthedimensionalityofthespacesuchthattheresultingprojectionerrorvanishes.Ourpreliminaryresultsshowthatitmayleadtoabetterclassi¿cationaccuracy,especiallyfordatawithhighintrinsicdimensionality.