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...
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...
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 ...
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...
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...