Campus Units
Aerospace Engineering, Materials Science and Engineering, Mechanical Engineering, Statistics
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
2019
Journal or Book Title
arXiv
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.
Copyright Owner
The Authors
Copyright Date
2019
Language
en
File Format
application/pdf
Recommended Citation
Thompson, Geoffrey Z.; Maitra, Ranjan; Meeker, William Q.; and Bastawros, Ashraf, "Classification with the matrix-variate-t distribution" (2019). Aerospace Engineering Publications. 148.
https://lib.dr.iastate.edu/aere_pubs/148
Comments
This is a pre-print of the article Thompson, Geoffrey Z., Ranjan Maitra, William Q. Meeker, and Ashraf Bastawros. "Classification with the matrix-variate-t distribution." arXiv preprint arXiv:1907.09565 (2019). Posted with permission.