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ICCV
1999
IEEE
13 years 10 months ago
Principal Manifolds and Bayesian Subspaces for Visual Recognition
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Three techniques: Principal Component Analy...
Baback Moghaddam
ICCV
2009
IEEE
14 years 10 months ago
Dimensionality Reduction and Principal Surfaces via Kernel Map Manifolds
We present a manifold learning approach to dimensionality reduction that explicitly models the manifold as a mapping from low to high dimensional space. The manifold is represen...
Samuel Gerber, Tolga Tasdizen, Ross Whitaker
EJC
2007
13 years 5 months ago
Euler characteristics and chromatic polynomials
Given a graph we show how to construct a family of manifolds whose Euler characteristics are the values of the chromatic polynomial of the graph at various integers. The manifolds...
Michael Eastwood, Stephen Huggett
PR
2007
137views more  PR 2007»
13 years 5 months ago
Boosted manifold principal angles for image set-based recognition
In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particula...
Tae-Kyun Kim, Ognjen Arandjelovic, Roberto Cipolla
PAMI
2002
114views more  PAMI 2002»
13 years 5 months ago
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
Baback Moghaddam