Coupled Projections for Semi-supervised Adaptation of Dictionaries

Data-driven dictionaries have produced state-of-theart results in various classification 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. The algorithm is modified to learn a common discriminative dictionary, which can be further used for classification. The algorithm can be use for adaptation across multiple domains and is extensible to non-linear feature space. 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. Further, it can also be used for heterogenous domain adaptation, where different features are extracted for different domains. Various recognition experiments show that the method performs on par or better than competitive state-of-the-art methods.