Forward stagewise regression on incomplete datasets

Abstract

The Forward Stagewise Regression (FSR) algorithm is a popular procedure to generate sparse linear regression models. However, the standard FSR assumes that the data are fully observed. This assumption is often flawed and pre-processing steps are applied to the dataset so that FSR can be used. In this paper, we extend the FSR algorithm to directly handle datasets with partially observed feature vectors, dismissing the need for the data to be pre-processed. Experiments were carried out on real-world datasets and the proposed method reported promising results when compared to the usual strategies for handling incomplete data.