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JMLR
2006
206views more  JMLR 2006»
14 years 11 months ago
New Algorithms for Efficient High-Dimensional Nonparametric Classification
This paper is about non-approximate acceleration of high-dimensional nonparametric operations such as k nearest neighbor classifiers. We attempt to exploit the fact that even if w...
Ting Liu, Andrew W. Moore, Alexander G. Gray
SIAMSC
2008
198views more  SIAMSC 2008»
14 years 11 months ago
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space
A model-constrained adaptive sampling methodology is proposed for reduction of large-scale systems with high-dimensional parametric input spaces. Our model reduction method uses a ...
T. Bui-Thanh, Karen Willcox, Omar Ghattas
PAMI
2010
276views more  PAMI 2010»
14 years 10 months ago
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
—This paper considers feature selection for data classification in the presence of a huge number of irrelevant features. We propose a new feature selection algorithm that addres...
Yijun Sun, Sinisa Todorovic, Steve Goodison
KDD
2001
ACM
253views Data Mining» more  KDD 2001»
16 years 6 days ago
GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces
The similarity join is an important operation for mining high-dimensional feature spaces. Given two data sets, the similarity join computes all tuples (x, y) that are within a dis...
Jens-Peter Dittrich, Bernhard Seeger
KDD
1998
ACM
190views Data Mining» more  KDD 1998»
15 years 4 months ago
Time Series Forecasting from High-Dimensional Data with Multiple Adaptive Layers
This paper describes our work in learning online models that forecast real-valued variables in a high-dimensional space. A 3GB database was collected by sampling 421 real-valued s...
R. Bharat Rao, Scott Rickard, Frans Coetzee