Learning Using Privileged Information

When applying machine learning techniques to real-world problems, prior knowledge plays a crucial role in enriching the learning system. This prior knowledge is typically defined by domain experts and can be integrated into machine learning algorithms in a variety of ways: as a preference of certain prediction functions over others, as a Bayesian prior over parameters, or as additional information about the samples in the training set used for learning a prediction function. The latter setup is called learning using privileged information (LUPI) and was adopted by Vapnik and Vashist in (Neural Netw, 2009). Formally, LUPI refers to the setting when, in addition to the main data modality, the learning system has access to an extra source of information about the training examples. The additional source of information is only available during training and therefore is called privileged. The main goal of LUPI is to utilize privileged information and to learn a better model in the main data modality than one would learn without the privileged source. As an illustration, for protein classification based on amino-acid sequences, the protein tertiary structure can be considered additional information. Another example is recognizing objects in images; the textual information in the form of image tags contains additional object descriptions and can be used as privileged.