  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, semi-supervised
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
semi-supervised 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 semi-supervised
classifier in sparse code space. We show that our approach provides an
improvement over state-of-the-art semi-supervised dictionary learning methods.
