Finite mixtures of matrix normal distributions for classifying three-way data |
| |
Authors: | Cinzia Viroli |
| |
Institution: | 1.Department of Statistics,University of Bologna,Bologna,Italy |
| |
Abstract: | Matrix-variate distributions represent a natural way for modeling random matrices. Realizations from random matrices are generated
by the simultaneous observation of variables in different situations or locations, and are commonly arranged in three-way
data structures. Among the matrix-variate distributions, the matrix normal density plays the same pivotal role as the multivariate
normal distribution in the family of multivariate distributions. In this work we define and explore finite mixtures of matrix
normals. An EM algorithm for the model estimation is developed and some useful properties are demonstrated. We finally show
that the proposed mixture model can be a powerful tool for classifying three-way data both in supervised and unsupervised
problems. A simulation study and some real examples are presented. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|