Robust Dimensionality Reduction: How to Increase Efficiency of Machine Learning Computations

Abstract

Artificial intelligence is nowadays equipped with a plethora of tools for obtaining relevant knowledge from (possibly big) data in a variety of tasks. While habitually used methods of machine learning applicable within artificial intelligence tools can be characterized as black boxes, practical applications often require to understand why a particular conclusion (e.g. decision) was made, which arouses interest in explainable machine learning. This chapter is devoted to variable selection methods for finding the most relevant variables for the given task. If statistically robust variable selection methods are exploited, the harmful influence of data contamination by outlying values on the results is typically eliminated or downweighted. Principles of prior, intrinsic, and posterior variable selection approaches are recalled and compared on three real datasets related to gene expression measurements, neighborhood crime rate, and tourism infrastructure. These examples reveal robust approaches to machine learning that outperform non-robust ones if the data are contaminated by outliers.