Abstract

Data-driven dictionaries have produced state-of-the-art results in various classifification tasks. However, when the target data has a different distribution than the source data, the learned sparse representation may not be optimal. In this paper, we investigate if it is possible to optimally represent both source and target by a common dictionary. Specifically, we describe a technique which jointly learns projections of data in the two domains, and a latent dictionary which can succinctly represent both the domains in the projected low-dimensional space. An effificient optimization technique is presented, which can be easily kernelized and extended to multiple domains. The algorithm is modifified to learn a common discriminative dictionary, which can be further used for classifification. The proposed approach does not require any explicit correspondence between the source and target domains, and shows good results even when there are only a few labels available in the target domain. Various recognition experiments show that the method performs on par or better than competitive stateof-the-art methods