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PAMI
2011
13 years 5 months ago
Multiple Kernel Learning for Dimensionality Reduction
—In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting ...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
ACII
2005
Springer
14 years 11 days ago
Simulated Annealing Based Hand Tracking in a Discrete Space
Hand tracking is a challenging problem due to the complexity of searching in a 20+ degrees of freedom (DOF) space for an optimal estimation of hand configuration. This paper repres...
Wei Liang, Yunde Jia, Yang Liu, Cheng Ge
ACMACE
2007
ACM
14 years 2 months ago
Application of dimensionality reduction techniques to HRTFS for interactive virtual environments
Fundamental to the generation of 3D audio is the HRTF processing of acoustical signals. Unfortunately, given the high dimensionality of HRTFs, incorporating them into dynamic/inte...
Bill Kapralos, Nathan Mekuz
CIBCB
2006
IEEE
14 years 4 months ago
Visualization of Support Vector Machines with Unsupervised Learning
– The visualization of support vector machines in realistic settings is a difficult problem due to the high dimensionality of the typical datasets involved. However, such visuali...
Lutz Hamel
TSP
2008
117views more  TSP 2008»
13 years 10 months ago
A Theory for Sampling Signals From a Union of Subspaces
One of the fundamental assumptions in traditional sampling theorems is that the signals to be sampled come from a single vector space (e.g. bandlimited functions). However, in many...
Yue M. Lu, Minh N. Do