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SCIA
2009
Springer
305views Image Analysis» more  SCIA 2009»
14 years 2 months ago
A Convex Approach to Low Rank Matrix Approximation with Missing Data
Many computer vision problems can be formulated as low rank bilinear minimization problems. One reason for the success of these problems is that they can be efficiently solved usin...
Carl Olsson, Magnus Oskarsson
PRL
2010
188views more  PRL 2010»
13 years 6 months ago
Sparsity preserving discriminant analysis for single training image face recognition
: Single training image face recognition is one of main challenges to appearance-based pattern recognition techniques. Many classical dimensionality reduction methods such as LDA h...
Lishan Qiao, Songcan Chen, Xiaoyang Tan
ECCV
2008
Springer
14 years 9 months ago
Higher Dimensional Affine Registration and Vision Applications
Abstract. Affine registration has a long and venerable history in computer vision literature, and extensive work have been done for affine registrations in IR2 and IR3 . In this pa...
Yu-Tseh Chi, S. M. Nejhum Shahed, Jeffrey Ho, Ming...
CORR
2011
Springer
167views Education» more  CORR 2011»
13 years 2 months ago
Fast global convergence of gradient methods for high-dimensional statistical recovery
Many statistical M-estimators are based on convex optimization problems formed by the weighted sum of a loss function with a norm-based regularizer. We analyze the convergence rat...
Alekh Agarwal, Sahand Negahban, Martin J. Wainwrig...
MCS
2005
Springer
14 years 1 months ago
A Probability Model for Combining Ranks
Mixed Group Ranks is a parametric method for combining rank based classiers that is eective for many-class problems. Its parametric structure combines qualities of voting methods...
Ofer Melnik, Yehuda Vardi, Cun-Hui Zhang