Manifold-Based Classifier Ensembles

Abstract

In this paper, we briefly present classifier ensembles making use of nonlinear manifolds. Riemannian manifolds have been created using classifier interactions which are presented as symmetric and positive-definite (SPD) matrices. Grassmann manifolds as some particular case of Riemannian manifolds are constructed using decision profiles. Experimental routine shows advantages of Riemannian geometry and nonlinear manifolds for classifier ensemble learning.