Classification with the matrix-variate-t distribution.

ABSTRACT: Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal or repeated measures. This paper develops an Expectation-Maximization algorithm for discriminant analysis and classification with matrix-variate t-distributions. The methodology shows promise on simulated datasets or when applied to the forensic matching of fractured surfaces or the classification of functional Magnetic Resonance, satellite or hand gestures images.

SUBMITTER: Thompson GZ 

PROVIDER: S-EPMC7954198 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature