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NIPS
2003
15 years 7 months ago
Kernel Dimensionality Reduction for Supervised Learning
We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
JAT
2006
69views more  JAT 2006»
15 years 5 months ago
Almost periodic Verblunsky coefficients and reproducing kernels on Riemann surfaces
We give an explicit parametrization of a set of almost periodic CMV matrices whose spectrum (is equal to the absolute continuous spectrum and) is a homogenous set E lying on the u...
Franz Peherstorfer, P. Yuditskii
PR
2007
88views more  PR 2007»
15 years 5 months ago
Robust kernel Isomap
Isomap is one of widely-used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling (metric multidimensional s...
Heeyoul Choi, Seungjin Choi
ICIP
2005
IEEE
16 years 7 months ago
Visual tracking via efficient kernel discriminant subspace learning
Robustly tracking moving objects in video sequences is one of the key problems in computer vision. In this paper we introduce a computationally efficient nonlinear kernel learning...
Chunhua Shen, Anton van den Hengel, Michael J. Bro...
CVPR
2004
IEEE
16 years 7 months ago
A Correlation-Based Model Prior for Stereo
All non-trivial stereo problems need model priors to deal with ambiguities and noise perturbations. To meet requirements of increasingly demanding tasks such as modeling for rende...
Yanghai Tsin, Takeo Kanade