Classification with the matrix-variate-t distribution

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2019_BastawrosAshraf_ClassificationMatrix.pdf (458.16 KB)
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2020-01-22
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Maitra, Ranjan
Meeker, William
Bastawros, Ashraf
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Bastawros, Ashraf
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Meeker, William
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Aerospace Engineering

The Department of Aerospace Engineering seeks to instruct the design, analysis, testing, and operation of vehicles which operate in air, water, or space, including studies of aerodynamics, structure mechanics, propulsion, and the like.

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The Department of Aerospace Engineering was organized as the Department of Aeronautical Engineering in 1942. Its name was changed to the Department of Aerospace Engineering in 1961. In 1990, the department absorbed the Department of Engineering Science and Mechanics and became the Department of Aerospace Engineering and Engineering Mechanics. In 2003 the name was changed back to the Department of Aerospace Engineering.

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  • Department of Aerospace Engineering and Engineering Mechanics (1990-2003)

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Mechanical Engineering
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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.
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This is a manuscript of an article published as Thompson, Geoffrey Z., Ranjan Maitra, William Q. Meeker, and Ashraf F. Bastawros. "Classification with the matrix-variate-t distribution." Journal of Computational and Graphical Statistics 29, no. 3 (2020): 668-674. doi:10.1080/10618600.2019.1696208. Posted with permission.
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http://doi.org/10.1080/10618600.2019.1696208
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https://dr.lib.iastate.edu/handle/20.500.12876/1994
Subject Categories
Multivariate Analysis
Statistical Methodology
Statistical Models
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Tue Jan 01 00:00:00 UTC 2019
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