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NIPS
2007
13 years 5 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 ...
IDEAL
2009
Springer
13 years 10 months ago
Supervised Feature Extraction Using Hilbert-Schmidt Norms
We propose a novel, supervised feature extraction procedure, based on an unbiased estimator of the Hilbert-Schmidt independence criterion (HSIC). The proposed procedure can be dire...
Povilas Daniusis, Pranas Vaitkus
ICML
2007
IEEE
14 years 4 months ago
Supervised feature selection via dependence estimation
We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The ...
Le Song, Alex J. Smola, Arthur Gretton, Karsten M....
ICML
2007
IEEE
14 years 4 months ago
A dependence maximization view of clustering
We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Inde...
Le Song, Alexander J. Smola, Arthur Gretton, Karst...