Semisupervised Dictionary Learning with Graph Regularized and Active Points
K. H. Tran, F. M. Ngolè Mboula, Jean‐Luc Starck, V. Prost · SIAM Journal on Imaging Sciences · 2020
Supervised dictionary learning has gained much interest in the recent decade and has shown significant performance improvements in image classification. However, in general, supervised learning needs a large number of labelled samples per class to achieve an acceptable result. In order to deal with databases which have just a few labelled samples per class, semisupervised learning, which also exploits unlabelled samples in training phase is used. Indeed, unlabelled samples can help to regularize the learning model, yielding an improvement of classification accuracy. In this paper, we propose a new semisupervised dictionary learning method based on two pillars: on one hand, we enforce manifold structure preservation from the original data into sparse code space using locally linear embedding, which can be considered a regularization of sparse code; on the other hand, we train a semisupervised classifier in sparse code space. We show that our approach provides an improvement over state-of-the-art semisupervised dictionary learning methods.