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Supervised Dictionary Learning has gained much interest in the recent decadeand has shown significant performance improvements in image classification.However, in general, supervised learning needs a large number of labelledsamples per class to achieve an acceptable result. In order to deal withdatabases which have just a few labelled samples per class, semi-supervisedlearning, which also exploits unlabelled samples in training phase is used.Indeed, unlabelled samples can help to regularize the learning model, yieldingan improvement of classification accuracy. In this paper, we propose a newsemi-supervised dictionary learning method based on two pillars: on one hand,we enforce manifold structure preservation from the original data into sparsecode space using Locally Linear Embedding, which can be considered aregularization of sparse code; on the other hand, we train a semi-supervisedclassifier in sparse code space. We show that our approach provides animprovement over state-of-the-art semi-supervised dictionary learning methods.

Semi-supervised dictionary learning with graph regularization and active points