Manifold learning, A promised land or work in progress?

Mei Chen Yeh, I. Hsiang Lee, Gang Wu, Yi Wu, Edward Y. Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Tasks of image clustering and classification often deal with data of very high dimensions. To alleviate the dimensionality curse, several methods, such as Isomap, LLE and KPCA, have recently been proposed and applied to learn low-dimensional, non-linear embedded manifolds in high-dimensional spaces. Unfortunately, the scenarios in which these methods appear to be effective are very contrived. In this work, we empirically examine these methods on a realistic but not-so-difficult dataset. We discuss the promises and limitations of these dimensionreduction schemes.

Original languageEnglish
Title of host publicationIEEE International Conference on Multimedia and Expo, ICME 2005
Pages1154-1157
Number of pages4
DOIs
Publication statusPublished - 2005 Dec 1
Externally publishedYes
EventIEEE International Conference on Multimedia and Expo, ICME 2005 - Amsterdam, Netherlands
Duration: 2005 Jul 62005 Jul 8

Publication series

NameIEEE International Conference on Multimedia and Expo, ICME 2005
Volume2005

Other

OtherIEEE International Conference on Multimedia and Expo, ICME 2005
CountryNetherlands
CityAmsterdam
Period05/7/605/7/8

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Yeh, M. C., Lee, I. H., Wu, G., Wu, Y., & Chang, E. Y. (2005). Manifold learning, A promised land or work in progress? In IEEE International Conference on Multimedia and Expo, ICME 2005 (pp. 1154-1157). [1521631] (IEEE International Conference on Multimedia and Expo, ICME 2005; Vol. 2005). https://doi.org/10.1109/ICME.2005.1521631