Semi-supervised nonlinear dimensionality reduction

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

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

75 Scopus citations


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 publicationICML 2006 - Proceedings of the 23rd International Conference on Machine Learning
Number of pages8
StatePublished - Oct 6 2006
EventICML 2006: 23rd International Conference on Machine Learning - Pittsburgh, PA, United States
Duration: Jun 25 2006Jun 29 2006


OtherICML 2006: 23rd International Conference on Machine Learning
Country/TerritoryUnited States
CityPittsburgh, PA

All Science Journal Classification (ASJC) codes

  • Engineering(all)


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