Boosting with Side Information

In many problems of machine learning and computer vision, there exists side information, i.e., information contained in the training data and not available in the testing phase. This motivates the recent development of a new learning approach known as learning with side information that aims to incorporate side information for improved learning algorithms. In this work, we describe a new training method of boosting classifiers that uses side information, which we term as AdaBoost+. In particular, AdaBoost+ employs a novel classification label imputation method to construct extra weak classifiers from the available information that simulate the performance of better weak classifiers obtained from the features in side information. We apply our method to two problems, namely handwritten digit recognition and facial expression recognition from low resolution images, where it demonstrates its effectiveness in classification performance.