Compensation Learning

Weighting strategy prevails in machine learning. For example, a common approach in robust machine learning is to exert lower weights on samples which are likely to be noisy or hard. This study reveals another undiscovered strategy, namely, compensating, that has also been widely used in machine learning. Learning with compensating is called compensation learning and a systematic taxonomy is constructed for it in this study. In our taxonomy, compensation learning is divided on the basis of the compensation targets, inference manners, and granularity levels. Many existing learning algorithms including some classical ones can be seen as a special case of compensation learning or partially leveraging compensating. Furthermore, a family of new learning algorithms can be obtained by plugging the compensation learning into existing learning algorithms. Specifically, three concrete new learning algorithms are proposed for robust machine learning. Extensive experiments on text sentiment analysis, image classification, and graph classification verify the effectiveness of the three new algorithms. Compensation learning can also be used in various learning scenarios, such as imbalance learning, clustering, regression, and so on.