Ensemble LDA via the modified Cholesky decomposition

Zhenguo Gao

Abstract:

A binary classification problem in the high-dimensional settings is studied via the
ensemble learning with each base classifier constructed from the linear discriminant
analysis (LDA), and these base classifiers are integrated by the weighted voting. The
precision matrix in the LDA rule is estimated by the modified Cholesky decomposition
(MCD), which is able to provide us with a set of precision estimates by considering
multiple variable orderings, and hence yield a group of different LDA classifiers. Such
available LDA classifiers are then integrated to improve the classification performance.
The simulation and the application studies are conducted to demonstrate the merits of the
proposed method.