Semi-supervised classification via discriminative sparse manifold regularization

• We proposed a newly semi-supervised manifold learning algorithm named Discriminative Sparse Manifold Regularization (DSMR) to classify.
• For each labeled or unlabeled sample, its dictionary is updated according to its property and use the new dictionary to reconstruct it.
• Extensive experiments on the several UCI data sets and face data sets demonstrate the effectiveness of the proposed DSMR.

In this paper, a newly semi-supervised manifold learning algorithm named Discriminative Sparse Manifold Regularization (DSMR) is proposed. In DSMR, the whole unlabeled sample set is used to reconstruct the mean vector of each class, then obtains the sparse coefficient. For each sample of labeled samples, the new dictionary is composed of samples from the same class and the samples from the unlabeled sample set according to the corresponding rows of the sparse coefficient. For each unlabeled sample, the new dictionary is composed of samples from the whole unlabeled samples and the samples from the labeled class according to the corresponding columns of the sparse coefficient. Additionally, a discriminative term is added to stabilize performance of the algorithm. Extensive experiments on the several UCI datasets and face datasets demonstrate the effectiveness of the proposed DSMR.