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

A Harol, EM Pekalska, S Verzakov, RPW Duin

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)

Abstract

Pairwiseproximitiesdescribethepropertiesofobjectsintermsoftheirsimilarities.Byusingdi¿erentdistance-basedfunctionsonemayencodedi¿erentcharacteristicsofagivenproblem.However,tousetheframeworkofstatisticalpatternrecognitionsomevectorrepresentationshouldbeconstructed.Oneofthesimplestwaystodothatistode¿neanisometricembeddingtosomevectorspace.Inthiswork,wewillfocusonalinearembeddingintoa(pseudo-)Euclideanspace. Thisisusuallywellde¿nedfortrainingdata.Someinadequacy,however,appearswhenprojectingnewortestobjectsduetotheresultingprojectionerrors.Inthispaperweproposeanaugmentedembeddingalgorithmthatenlargesthedimensionalityofthespacesuchthattheresultingprojectionerrorvanishes.Ourpreliminaryresultsshowthatitmayleadtoabetterclassi¿cationaccuracy,especiallyfordatawithhighintrinsicdimensionality.
Original languageUndefined/Unknown
Title of host publicationStructural, syntactic and statistical pattern recognition
EditorsDY Yeung, JT Kwok, A Fred, F Roli, D de Ridder
Place of PublicationHeidelberg
PublisherSpringer
Pages613-621
Number of pages9
Publication statusPublished - 2006
EventJoint IAPR International Workshops SSPR 2006 and SPR 2006, Hong Kong, China - Heidelberg
Duration: 17 Aug 200619 Aug 2006

Publication series

Name
PublisherSpringer
NameLecture Notes in Computer Science
Volume4109
ISSN (Print)0302-9743

Conference

ConferenceJoint IAPR International Workshops SSPR 2006 and SPR 2006, Hong Kong, China
Period17/08/0619/08/06

Keywords

  • conference contrib. refereed
  • CWTS 0.75 <= JFIS < 2.00

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