An outlier-insensitive linear pursuit embedding algorithm

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

Research output: Chapter in Book/Published conference outputConference publication


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
Number of pages5
ISBN (Print)978-1-4244-3703-0
Publication statusPublished - 10 Nov 2009
Event2009 International Conference on Machine Learning and Cybernetics - Hebei, China
Duration: 12 Jul 200915 Jul 2009


Conference2009 International Conference on Machine Learning and Cybernetics


  • dimension reduction
  • manifold learning
  • outliers


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