Random projections of Fischer Linear Discriminant classifier for multi-class classification

Ensembling classifiers has been an effective technique for improving performance and generalizability of classification tasks. In a recent research direction, the ensemble of the random projections is being utilized as an effective regularization technique with linear discriminant classifiers. However the framework has only been designed for binary classifiers. In this paper we extend the idea for the multiclass classifiers, which directly improves the applicability of the framework to a broader class of problems. We performed experiments with multiple high-dimensional benchmark datasets, and compare the performance of our framework with other state-of-the-art methods for multi-class classification. We also extend the theoretical error bounds for misclassification to provide a theoretical analysis. Results demonstrate the efficacy of our methodology.