A multilabel extension of LDA based on the gram-schmidt orthogonalization procedure

Abstract

Multilabel classification is a generalization of the traditional unidimensional classification problem, the goal of multilabel classification is to learn a function that maps instances into a set of relevant labels. This article proposes an extension to linear discriminant analysis in the context of multilabel classification. The new method is based on Gram-Schmidt orthogonalization procedure. The theoretical basis and underlying assumptions of the new model are described and the method is experimentally evaluated on the Emotions data set for multilabel classification. The analysis of the empirical results support that this new method is competitive and in some instances superior to the baseline.