A supervised discriminant subspaces-based ensemble learning for binary classification

To enable feature extraction and reduction in pattern recognition applications, discriminant subspace analysis-based algorithms are used. Among the better-known discriminant subspace techniques for two-pattern recognition is the Fukunaga-Koontz transform (FKT). This technique has been modified to a non-linear version with the aid of kernel machines. This has enabled an increase in its non-linear discrimination ability, apart from securing higher statistics of data. The performance of kernel FKT (KFKT) is, however, dependent on the choice of suitable kernels and their inherent parameters. The aim of this paper is to ascertain the difficulties of ensemble learning with a finite set of base kernels on FKT subspaces. The study presents a new approach to tackling the issues of multiple kernel learning (MKL) on FKT. For this, a better kernel function is designed by either linearly or non-linearly combining numerous pre-chosen kernels into the algorithm. KFKTs were used with sub-kernel learners with a diverse set of kernels, each with different parameters. Weighted and unweighted fusions were employed in order to combine the predictions of sub-learners. The eventual results proved that the ensemble of KFKTs was far better than the single KFKTs as far as classification performance went.