Semi-supervised nonlinear dimensionality reduction

Xin Yang, Haoying Fu, Hongyuan Zha, Jesse Louis Barlow

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

38 Scopus citations

Abstract

The problem of nonlinear dimensionality reduction is considered. We focus on problems where prior information is available, namely, semi-supervised dimensionality reduction. It is shown that basic nonlinear dimensionality reduction algorithms, such as Locally Linear Embedding (LLE), Isometric feature mapping (ISOMAP), and Local Tangent Space Alignment (LTSA), can be modified by taking into account prior information on exact mapping of certain data points. The sensitivity analysis of our algorithms shows that prior information will improve stability of the solution. We also give some insight on what kind of prior information best improves the solution. We demonstrate the usefulness of our algorithm by synthetic and real life examples.

Original languageEnglish (US)
Title of host publicationACM International Conference Proceeding Series - Proceedings of the 23rd International Conference on Machine Learning, ICML 2006
Pages1065-1072
Number of pages8
DOIs
StatePublished - Dec 1 2006
Event23rd International Conference on Machine Learning, ICML 2006 - Pittsburgh, PA, United States
Duration: Jun 25 2006Jun 29 2006

Publication series

NameACM International Conference Proceeding Series
Volume148

Other

Other23rd International Conference on Machine Learning, ICML 2006
CountryUnited States
CityPittsburgh, PA
Period6/25/066/29/06

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Semi-supervised nonlinear dimensionality reduction'. Together they form a unique fingerprint.

  • Cite this

    Yang, X., Fu, H., Zha, H., & Barlow, J. L. (2006). Semi-supervised nonlinear dimensionality reduction. In ACM International Conference Proceeding Series - Proceedings of the 23rd International Conference on Machine Learning, ICML 2006 (pp. 1065-1072). (ACM International Conference Proceeding Series; Vol. 148). https://doi.org/10.1145/1143844.1143978