An outlier-insensitive linear pursuit embedding algorithm

Yan-Wei Pang, Xin Lu, Yuan Yuan, Jing Pan

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

Abstract

Dimension reduction techniques with the flexibility to learn a broad class of nonlinear manifold have attracted increasingly close attention since meaningful low-dimensional structures are always hidden in large number of high-dimensional natural data, such as global climate patterns, images of a face under different viewing conditions, etc. In this paper, we introduce L1-Norm Linear Pursuit Embedding (L1-LPE) algorithm, aims to find a more robust linear method in presence of outliers and unexpected samples when dealing with high-dimensional nonlinear manifold problems. To achieve this goal, a new method based on a rather different geometric intuition L1-Norm is proposed to describe the local geometric structure. L1-LPE and L2-LPE is studied and compared in this paper and experiments on both toy problems and real data problems are presented.

Original languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Machine Learning and Cybernetics
PublisherIEEE
Pages2792-2796
Number of pages5
Volume5
ISBN (Print)978-1-4244-3703-0
DOIs
Publication statusPublished - 10 Nov 2009
Event2009 International Conference on Machine Learning and Cybernetics - Hebei, China
Duration: 12 Jul 200915 Jul 2009

Conference

Conference2009 International Conference on Machine Learning and Cybernetics
CountryChina
CityHebei
Period12/07/0915/07/09

Keywords

  • dimension reduction
  • manifold learning
  • outliers

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  • Cite this

    Pang, Y-W., Lu, X., Yuan, Y., & Pan, J. (2009). An outlier-insensitive linear pursuit embedding algorithm. In Proceedings of the 2009 International Conference on Machine Learning and Cybernetics (Vol. 5, pp. 2792-2796). IEEE. https://doi.org/10.1109/ICMLC.2009.5212621