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» Minimization of an M-convex Function
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PRL
1998
132views more  PRL 1998»
13 years 9 months ago
Unsupervised feature selection using a neuro-fuzzy approach
A neuro-fuzzy methodology is described which involves connectionist minimization of a fuzzy feature evaluation index with unsupervised training. The concept of a ¯exible membersh...
Jayanta Basak, Rajat K. De, Sankar K. Pal
INFORMS
1998
100views more  INFORMS 1998»
13 years 9 months ago
Feature Selection via Mathematical Programming
The problem of discriminating between two nite point sets in n-dimensional feature space by a separating plane that utilizes as few of the features as possible, is formulated as a...
Paul S. Bradley, Olvi L. Mangasarian, W. Nick Stre...
CVPR
2010
IEEE
14 years 5 months ago
Learning Shift-Invariant Sparse Representation of Actions
A central problem in the analysis of motion capture (Mo- Cap) data is how to decompose motion sequences into primitives. Ideally, a description in terms of primitives should fac...
Yi Li
IJCV
2002
129views more  IJCV 2002»
13 years 9 months ago
A Hierarchical Symmetric Stereo Algorithm Using Dynamic Programming
In this paper, a new hierarchical stereo algorithm is presented. The algorithm matches individual pixels in corresponding scanlines by minimizing a cost function. Several cost fun...
G. Van Meerbergen, Maarten Vergauwen, Marc Pollefe...
ICASSP
2011
IEEE
13 years 1 months ago
Low-rank matrix completion with geometric performance guarantees
—The low-rank matrix completion problem can be stated as follows: given a subset of the entries of a matrix, find a low-rank matrix consistent with the observations. There exist...
Wei Dai, Ely Kerman, Olgica Milenkovic