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Multimodal Data Representations with Parameterized Local Structures

  • Ying Zhu
  • Dorin Comaniciu
  • Stuart Schwartz
  • Visvanathan Ramesh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2350)

Abstract

In many vision problems, the observed data lies in a nonlinear manifold in a high-dimensional space. This paper presents a generic modelling scheme to characterize the nonlinear structure of the manifold and to learn its multimodal distribution. Our approach represents the data as a linear combination of parameterized local components, where the statistics of the component parameterization describe the nonlinear structure of the manifold. The components are adaptively selected from the training data through a progressive density approximation procedure, which leads to the maximum likelihood estimate of the underlying density. We show results on both synthetic and real training sets, and demonstrate that the proposed scheme has the ability to reveal important structures of the data.

Keywords

Function Association Initial Cluster Nonlinear Structure Local Component Multimodal Distribution 
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 2002

Authors and Affiliations

  • Ying Zhu
    • 1
  • Dorin Comaniciu
    • 2
  • Stuart Schwartz
    • 1
  • Visvanathan Ramesh
    • 2
  1. 1.Department of Electrical EngineeringPrinceton UniversityPrincetonUSA
  2. 2.Imaging & Visualization DepartmentSiemens Corporate ResearchPrincetonUSA

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