Semi-supervised dual graph regularized dictionary learning

Dictionary Learning (DL) encompasses methods and algorithms thataim at deriving a set of cardinal features which enables one to concisely describe signals of a given type. The benefit of such dictionaries in sparsity-driven signal recovery hasbeen shown in several applications (see for example [1, 2, 3, 4]). In numerous applications of machine learning, data are labelled and/orsampled from some regular manifold; thus it is suitable, for classification or interpolation tasks for instance, that the learned codes allow for a better discrimination of the data samples with respect to labels information or manifold's structure. The growing field of supervised dictionary learning precisely consists of DL methods that account for these additional information(a recent review can be found in [5]). Unlike the supervised classification in which only labelled data is used to train the classifier, the unlabelled data is also used in training to make use of all the manifold's structure information available.