SVD Feature Selection for Probabilistic Taxonomy Learning

Abstract

In this paper, we propose a novel way to include unsupervised feature selection methods in probabilistic taxonomy learning models. We leverage on the computation of logistic regression to exploit unsupervised feature selection of singular value decomposition (SVD). Experiments show that this way of using SVD for feature selection positively affects performances.