Mahalanobis-based adaptive nonlinear dimension reduction

Djamila Aouada, Yuliy Baryshnikov, Hamid Krim

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

1 Citation (Scopus)

Abstract

We define a new adaptive embedding approach for data dimension reduction applications. Our technique entails a local learning of the manifold of the initial data, with the objective of defining local distance metrics that take into account the different correlations between the data points. We choose to illustrate the properties of our work on the isomap algorithm. We show through multiple simulations that the new adaptive version of isomap is more robust to noise than the original non-adaptive one.

Original languageEnglish
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages742-745
Number of pages4
DOIs
Publication statusPublished - Nov 18 2010
Externally publishedYes
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: Aug 23 2010Aug 26 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period8/23/108/26/10

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Cite this

Aouada, D., Baryshnikov, Y., & Krim, H. (2010). Mahalanobis-based adaptive nonlinear dimension reduction. In Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 (pp. 742-745). [5596035] (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2010.187