Semi-supervised metric learning VIA topology representation

Q. Y. Wang*, Pong Chi YUEN, G. C. Feng

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Learning an appropriate distance metric is a critical problem in pattern recognition. This paper addresses the problem in semi-supervised metric learning and proposes a new regularized semi-supervised metric learning (RSSML) method using local topology and triplet constraint. Our regularizer is designed and developed based on local topology, which is represented by local neighbors from the local smoothness, cluster (region density) and manifold information point of view. The regularizer is then combined with the large margin hinge loss on triplet constraint. We have implemented experiments on classification using UCI data set and KTH human action data set to evaluate the proposed method. Experimental results show that the proposed method outperforms state-of-the-art semi-supervised distance metric learning algorithms.

Original languageEnglish
Title of host publicationProceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
PublisherIEEE
Pages639-643
Number of pages5
ISBN (Print)9781467310680
Publication statusPublished - Aug 2012
Event20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest, Romania
Duration: 27 Aug 201231 Aug 2012

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference20th European Signal Processing Conference, EUSIPCO 2012
Country/TerritoryRomania
CityBucharest
Period27/08/1231/08/12

Scopus Subject Areas

  • Signal Processing
  • Electrical and Electronic Engineering

User-Defined Keywords

  • cluster
  • density
  • manifold
  • semi-supervised metric learning

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