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Sub-sampling for Efficient Spectral Mesh Processing

  • Rong Liu
  • Varun Jain
  • Hao Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4035)

Abstract

In this paper, we apply Nyström method, a sub-sampling and reconstruction technique, to speed up spectral mesh processing. We first relate this method to Kernel Principal Component Analysis (KPCA). This enables us to derive a novel measure in the form of a matrix trace, based soly on sampled data, to quantify the quality of Nyström approximation. The measure is efficient to compute, well-grounded in the context of KPCA, and leads directly to a greedy sampling scheme via trace maximization. On the other hand, analyses show that it also motivates the use of the max-min farthest point sampling, which is a more efficient alternative. We demonstrate the effectiveness of Nyström method with farthest point sampling, compared with random sampling, using two applications: mesh segmentation and mesh correspondence.

Keywords

Point Sampling Quality Measure Spectral Cluster Kernel Principal Component Analysis Matrix Trace 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rong Liu
    • 1
  • Varun Jain
    • 1
  • Hao Zhang
    • 1
  1. 1.GrUVi Lab, School of Computing SciencesSFUCanada

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