Similarity learning for semi-supervised multi-class boosting

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

*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

In semi-supervised classification boosting, a similarity measure is demanded in order to measure the distance between samples (both labeled and unlabeled). However, most of the existing methods employed a simple metric, such as Euclidian distance, which may not be able to truly reflect the actual similarity/distance. This paper presents a novel similarity learning method based on the geodesic distance. It incorporates the manifold, margin and the density information of the data which is important in semi-supervised classification. The proposed similarity measure is then applied to a semi-supervised multi-class boosting (SSMB) algorithm. In turn, the three semi-supervised assumptions, namely smoothness, low density separation and manifold assumption, are all satisfied. We evaluate the proposed method on UCI databases. Experimental results show that the SSMB algorithm with proposed similarity measure outperforms the SSMB algorithm with Euclidian distance.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2164-2167
Number of pages4
DOIs
Publication statusPublished - 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period22/05/1127/05/11

Scopus Subject Areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

User-Defined Keywords

  • assumption
  • boosting
  • density
  • manifold
  • margin
  • multi-class
  • semi-supervised learning
  • similarity

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