NCJ Number
303229
Journal
Journal of Computational and Graphical Statistics Volume: 29 Issue: 3 Dated: 2020 Pages: 668-674
Date Published
January 2020
Annotation
This article develops an expectation-maximization algorithm for discriminant analysis and classification with matrix-variate t-distributions.
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. The methodology presented in the current article shows promise on simulated datasets or when applied to the forensic matching of fractured surfaces or to the classification of functional magnetic resonance, satellite, or hand gestures images. (publisher abstract modified)
Date Published: January 1, 2020
Downloads
Similar Publications
- CaDAVEr: a metagenome-assembled genome catalog of microbial decomposers across vertebrate environments
- Detecting fentanyl analogs in counterfeit pharmaceuticals by surface-enhanced Raman spectroscopy using handheld Raman spectrometers
- Evaluation of the Occurrence and Associative Value of NonIdentifiable Fingermarks on Unfired Ammunition in Handguns for Evidence Supporting Proof of Criminal Possession, Use and Intent