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COLT
2010
Springer
13 years 7 months ago
Principal Component Analysis with Contaminated Data: The High Dimensional Case
We consider the dimensionality-reduction problem (finding a subspace approximation of observed data) for contaminated data in the high dimensional regime, where the number of obse...
Huan Xu, Constantine Caramanis, Shie Mannor
SIAMSC
2008
167views more  SIAMSC 2008»
13 years 9 months ago
High Order Numerical Quadratures to One Dimensional Delta Function Integrals
Abstract. We study high order numerical quadratures to one dimensional delta function integrals in this paper. This is motivated by the fact that traditional numerical quadratures ...
Xin Wen
ICDE
2003
IEEE
160views Database» more  ICDE 2003»
14 years 11 months ago
HD-Eye - Visual Clustering of High dimensional Data
Clustering of large data bases is an important research area with a large variety of applications in the data base context. Missing in most of the research efforts are means for g...
Alexander Hinneburg, Daniel A. Keim, Markus Wawryn...
IJCAI
1997
13 years 11 months ago
Is Nonparametric Learning Practical in Very High Dimensional Spaces?
Many of the challenges faced by the £eld of Computational Intelligence in building intelligent agents, involve determining mappings between numerous and varied sensor inputs and ...
Gregory Z. Grudic, Peter D. Lawrence
CVPR
2004
IEEE
14 years 12 months ago
Feature Selection for Classifying High-Dimensional Numerical Data
Classifying high-dimensional numerical data is a very challenging problem. In high dimensional feature spaces, the performance of supervised learning methods suffer from the curse...
Yimin Wu, Aidong Zhang