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» Unsupervised Learning of Manifolds via Linear Approximations
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ECCV
2010
Springer
15 years 1 months ago
Efficient Highly Over-Complete Sparse Coding using a Mixture Model
Sparse coding of sensory data has recently attracted notable attention in research of learning useful features from the unlabeled data. Empirical studies show that mapping the data...
ECCV
2006
Springer
15 years 11 months ago
Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers
Filtering based algorithms have become popular in tracking human body pose. Such algorithms can suffer the curse of dimensionality due to the high dimensionality of the pose state ...
Rui Li, Ming-Hsuan Yang, Stan Sclaroff, Tai-Peng T...
ICIAP
2001
Springer
15 years 9 months ago
Recognition of Shape-Changing Hand Gestures Based on Switching Linear Model
We present a method to track and recognize shape-changing hand gestures simultaneously. The switching linear model using active contour model well corresponds to temporal shapes a...
Mun Ho Jeong, Yoshinori Kuno, Nobutaka Shimada, Yo...
CVPR
2010
IEEE
15 years 5 months ago
Efficient Additive Kernels via Explicit Feature Maps
Maji and Berg [13] have recently introduced an explicit feature map approximating the intersection kernel. This enables efficient learning methods for linear kernels to be applied...
Andrea Vedaldi, Andrew Zisserman
TKDE
2011
479views more  TKDE 2011»
14 years 4 months ago
Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction
—Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis (LDA) is a traditional algorithm for supervised feat...
Wei Zhang, Zhouchen Lin, Xiaoou Tang