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» Outlier Detection for High Dimensional Data
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ICONIP
2004
14 years 10 months ago
Outliers Treatment in Support Vector Regression for Financial Time Series Prediction
Recently, the Support Vector Regression (SVR) has been applied in the financial time series prediction. The financial data are usually highly noisy and contain outliers. Detecting ...
Haiqin Yang, Kaizhu Huang, Laiwan Chan, Irwin King...
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ICCV
2007
IEEE
15 years 11 months ago
High-Dimensional Feature Matching: Employing the Concept of Meaningful Nearest Neighbors
Matching of high-dimensional features using nearest neighbors search is an important part of image matching methods which are based on local invariant features. In this work we hi...
Dusan Omercevic, Ondrej Drbohlav, Ales Leonardis
ICML
2006
IEEE
15 years 10 months ago
Bayesian regression with input noise for high dimensional data
This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...
Jo-Anne Ting, Aaron D'Souza, Stefan Schaal
TSP
2008
117views more  TSP 2008»
14 years 9 months ago
Sample Eigenvalue Based Detection of High-Dimensional Signals in White Noise Using Relatively Few Samples
The detection and estimation of signals in noisy, limited data is a problem of interest to many scientific and engineering communities. We present a mathematically justifiable, com...
R. R. Nadakuditi, A. Edelman
APVIS
2010
14 years 7 months ago
Interactive local clustering operations for high dimensional data in parallel coordinates
In this paper, we propose an approach of clustering data in parallel coordinates through interactive local operations. Different from many other methods in which clustering is glo...
Peihong Guo, He Xiao, Zuchao Wang, Xiaoru Yuan