[Zurück]

U. Radojicic, K. Nordhausen, J. Virta:
"Kurtosis-based projection pursuit for matrix-valued data";
Vortrag: Twenty-eight International Workshop on Matrices and Statistics, Manipal, India; 13.12.2021 - 15.12.2021.

We develop projection pursuit for data that admit a natural representation in matrix form.
For projection indices we propose extensions of the classical kurtosis and Mardia´s multivari-
ate kurtosis. The first index estimates projections for both sides of the matrices simultane-
ously, while the second index finds the two projections separately. Both indices are shown
to recover the optimally separating projection for two-group Gaussian mixtures in the full
absence of any label information. We further establish the strong consistency of the corre-
sponding sample estimators. Simulations and a real data example on hand-written postal
code data are used to demonstrate the method.

discriminant analysis, matrix-variate Gaussian mixture, rank-1 projection