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» Regression on manifolds using kernel dimension reduction
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PR
2006
89views more  PR 2006»
13 years 6 months ago
Gaussian fields for semi-supervised regression and correspondence learning
Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be us...
Jakob J. Verbeek, Nikos A. Vlassis
CGF
2010
111views more  CGF 2010»
13 years 6 months ago
One Point Isometric Matching with the Heat Kernel
A common operation in many geometry processing algorithms consists of finding correspondences between pairs of shapes by finding structure-preserving maps between them. A particul...
Maks Ovsjanikov, Quentin Mérigot, Facundo M...
ICDE
2002
IEEE
91views Database» more  ICDE 2002»
13 years 11 months ago
Lossy Reduction for Very High Dimensional Data
We consider the use of data reduction techniques for the problem of approximate query answering. We focus on applications for which accurate answers to selective queries are requi...
Chris Jermaine, Edward Omiecinski
ICPR
2006
IEEE
14 years 7 months ago
Motion Dependent Spatiotemporal Smoothing for Noise Reduction in Very Dim Light Image Sequences
A new method for noise reduction using spatiotemporal smoothing is presented in this paper. The method is developed especially for reducing the noise that arises when acquiring vi...
Henrik Malm, Eric Warrant
UAI
2000
13 years 8 months ago
The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data
This paper is about the use of metric data structures in high-dimensionalor non-Euclidean space to permit cached sufficientstatisticsaccelerationsof learning algorithms. It has re...
Andrew W. Moore