Semi-supervised classification with privileged information

The privileged information that is available only for the training examples and not available for test examples, is a new concept proposed by Vapnik and Vashist (Neural Netw 22(5–6):544–557, 2009). With the help of the privileged information, learning using privileged information (LUPI) (Neural Netw 22(5–6):544–557, 2009) can significantly accelerate the speed of learning. However, LUPI is a standard supervised learning method. In fact, in many real-world problems, there are also a lot of unlabeled data. This drives us to solve problems under a semi-supervised learning framework. In this paper, we propose a semi-supervised learning using privileged information (called Semi-LUPI), which can exploit both the distribution information in unlabeled data and privileged information to improve the efficiency of the learning. Furthermore, we also compare the relative importance of both types of information for the learning model. All experiments verify the effectiveness of the proposed method, and simultaneously show that Semi-LUPI can obtain superior performances over traditional supervised and semi-supervised methods.