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ECCV
2002
Springer

Multimodal Data Representations with Parameterized Local Structures

14 years 5 months ago
Multimodal Data Representations with Parameterized Local Structures
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.
Ying Zhu, Dorin Comaniciu, Stuart C. Schwartz, Vis
Added 16 Oct 2009
Updated 16 Oct 2009
Type Conference
Year 2002
Where ECCV
Authors Ying Zhu, Dorin Comaniciu, Stuart C. Schwartz, Visvanathan Ramesh
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