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» Feature Selection in Clustering Problems
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AAAI
2011
12 years 8 months ago
A Feasible Nonconvex Relaxation Approach to Feature Selection
Variable selection problems are typically addressed under a penalized optimization framework. Nonconvex penalties such as the minimax concave plus (MCP) and smoothly clipped absol...
Cuixia Gao, Naiyan Wang, Qi Yu, Zhihua Zhang
IPPS
2006
IEEE
14 years 2 months ago
On-the-fly kernel updates for high-performance computing clusters
High-performance computing clusters running longlived tasks currently cannot have kernel software updates applied to them without causing system downtime. These clusters miss oppo...
Kristis Makris, Kyung Dong Ryu
TNN
2010
154views Management» more  TNN 2010»
13 years 3 months ago
Discriminative semi-supervised feature selection via manifold regularization
We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number ...
Zenglin Xu, Irwin King, Michael R. Lyu, Rong Jin
SDM
2010
SIAM
168views Data Mining» more  SDM 2010»
13 years 7 months ago
Convex Principal Feature Selection
A popular approach for dimensionality reduction and data analysis is principal component analysis (PCA). A limiting factor with PCA is that it does not inform us on which of the o...
Mahdokht Masaeli, Yan Yan, Ying Cui, Glenn Fung, J...
NIPS
2004
13 years 10 months ago
The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space
A new distance measure between probability density functions (pdfs) is introduced, which we refer to as the Laplacian pdf distance. The Laplacian pdf distance exhibits a remarkabl...
Robert Jenssen, Deniz Erdogmus, José Carlos...