  In this paper, we propose a semi-supervised dictionary learning method that
uses both the information in labelled and unlabelled data and jointly trains a
linear classifier embedded on the sparse codes. The manifold structure of the
data in the sparse code space is preserved using the same approach as the
Locally Linear Embedding method (LLE). This enables one to enforce the
predictive power of the unlabelled data sparse codes. We show that our approach
provides significant improvements over other methods. The results can be
further improved by training a simple nonlinear classifier as SVM on the sparse
codes.
