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NIPS
2007
14 years 11 months ago
Random Features for Large-Scale Kernel Machines
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The feat...
Ali Rahimi, Benjamin Recht
ICML
2007
IEEE
15 years 10 months ago
Regression on manifolds using kernel dimension reduction
We study the problem of discovering a manifold that best preserves information relevant to a nonlinear regression. Solving this problem involves extending and uniting two threads ...
Jens Nilsson, Fei Sha, Michael I. Jordan
CVPR
2003
IEEE
15 years 11 months ago
Video-Based Face Recognition Using Probabilistic Appearance Manifolds
This paper presents a novel method to model and recognize human faces in video sequences. Each registered person is represented by a low-dimensional appearance manifold in the amb...
Kuang-Chih Lee, Jeffrey Ho, Ming-Hsuan Yang, David...
ICML
2008
IEEE
15 years 10 months ago
An RKHS for multi-view learning and manifold co-regularization
Inspired by co-training, many multi-view semi-supervised kernel methods implement the following idea: find a function in each of multiple Reproducing Kernel Hilbert Spaces (RKHSs)...
Vikas Sindhwani, David S. Rosenberg
MM
2006
ACM
203views Multimedia» more  MM 2006»
15 years 3 months ago
Learning image manifolds by semantic subspace projection
In many image retrieval applications, the mapping between highlevel semantic concept and low-level features is obtained through a learning process. Traditional approaches often as...
Jie Yu, Qi Tian