Add to ORKGMany machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion. In this paper, we extend this criterion to deal with structured and interdependent observations. This is achieved by modeling the structures using undirected graphical models and comparing the Hilbert space embeddings of distributions. We apply this new criterion to independent component analysis and sequence clustering
Detection of statistical dependence between random variables is an essential component in many machi...
We introduce two new functionals, the constrained covariance and the kernel mutual information, to ...
This work is centred on investigating dependencies and representing learned structures as graphs. W...
Many machine learning algorithms can be formulated in the framework of statistical independence such...
Many machine learning algorithms can be formulated in the framework of statis-tical independence suc...
We propose an independence criterion based on the eigenspectrum of covariance operators in reproduci...
Whereas kernel measures of independence have been widely applied in machine learning (notably in ker...
We introduce two new functionals, the constrained covariance and the kernel mutual information, to m...
Although kernel measures of independence have been widely applied in machine learning (notably in ke...
Kernel techniques are among the most popular and powerful approaches of data science. Among the key ...
© 2017, Springer-Verlag Berlin Heidelberg. Measures of statistical dependence between random variabl...
Many problems in unsupervised learning require the analysis of features of probability distributions...
Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible fram...
In recent work by (Song et al., 2007), it has been proposed to perform clustering by maximizing a Hi...
A statistical test of independence may be constructed using the Hilbert-Schmidt Independence Criteri...
Detection of statistical dependence between random variables is an essential component in many machi...
We introduce two new functionals, the constrained covariance and the kernel mutual information, to ...
This work is centred on investigating dependencies and representing learned structures as graphs. W...
Many machine learning algorithms can be formulated in the framework of statistical independence such...
Many machine learning algorithms can be formulated in the framework of statis-tical independence suc...
We propose an independence criterion based on the eigenspectrum of covariance operators in reproduci...
Whereas kernel measures of independence have been widely applied in machine learning (notably in ker...
We introduce two new functionals, the constrained covariance and the kernel mutual information, to m...
Although kernel measures of independence have been widely applied in machine learning (notably in ke...
Kernel techniques are among the most popular and powerful approaches of data science. Among the key ...
© 2017, Springer-Verlag Berlin Heidelberg. Measures of statistical dependence between random variabl...
Many problems in unsupervised learning require the analysis of features of probability distributions...
Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible fram...
In recent work by (Song et al., 2007), it has been proposed to perform clustering by maximizing a Hi...
A statistical test of independence may be constructed using the Hilbert-Schmidt Independence Criteri...
Detection of statistical dependence between random variables is an essential component in many machi...
We introduce two new functionals, the constrained covariance and the kernel mutual information, to ...
This work is centred on investigating dependencies and representing learned structures as graphs. W...