A Taxonomy for Learning with Perturbation and Algorithms

Abstract

Weighting strategy prevails in machine learning. For example, a common approach in robust machine learning is to exert low weights on samples which are likely to be noisy or quite hard. This study summarizes another less-explored strategy, namely, perturbation. Various incarnations of perturbation have been utilized but it has not been explicitly revealed. Learning with perturbation is called perturbation learning and a systematic taxonomy is constructed for it in this study. In our taxonomy, learning with perturbation is divided on the basis of the perturbation targets, directions, inference manners, and granularity levels. Many existing learning algorithms including some classical ones can be understood with the constructed taxonomy. Alternatively, these algorithms share the same component, namely, perturbation in their procedures. Furthermore, a family of new learning algorithms can be obtained by varying existing learning algorithms with our taxonomy. Specifically, three concrete new learning algorithms are proposed for robust machine learning. Extensive experiments on image classification and text sentiment analysis verify the effectiveness of the three new algorithms. Learning with perturbation can also be used in other various learning scenarios, such as imbalanced learning, clustering, regression, and so on.