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» Measuring Statistical Dependence with Hilbert-Schmidt Norms
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ALT
2005
Springer
14 years 3 months ago
Measuring Statistical Dependence with Hilbert-Schmidt Norms
Abstract. We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate ...
Arthur Gretton, Olivier Bousquet, Alex J. Smola, B...
ICML
2007
IEEE
14 years 7 months ago
A kernel-based causal learning algorithm
We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operato...
Xiaohai Sun, Dominik Janzing, Bernhard Schölk...
NIPS
2007
13 years 7 months ago
A Kernel Statistical Test of Independence
Although kernel measures of independence have been widely applied in machine learning (notably in kernel ICA), there is as yet no method to determine whether they have detected st...
Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le ...
SDM
2009
SIAM
191views Data Mining» more  SDM 2009»
14 years 3 months ago
Adaptive Concept Drift Detection.
An established method to detect concept drift in data streams is to perform statistical hypothesis testing on the multivariate data in the stream. Statistical decision theory off...
Anton Dries, Ulrich Rückert
ICIP
2003
IEEE
14 years 7 months ago
A hidden Markov model based framework for recognition of humans from gait sequences
In this paper we propose a generic framework based on Hidden Markov Models (HMMs) for recognition of individuals from their gait. The HMM framework is suitable, because the gait o...
Aravind Sundaresan, Amit K. Roy Chowdhury, Rama Ch...