Classification via semi-supervised multi-random subspace sparse representation

In this paper, we combine the random subspace and multi-view together and obtain a novel approach named semi-supervised multi-random subspace sparse representation (SSM-RSSR). In the proposed SSM-RSSR, firstly, we use subspace sparse representation to obtain the graph to characterize the distribution of samples in each subspace. Then, we fuse these graphs in the viewpoint of multi-view through an alternating optimization method and obtain the optimal coefficients of all random subspaces. Finally, we train a linear classifier under the framework of manifold regularization (MR) to obtain the final classified results. Through fusing the random subspaces, the proposed SSM-RSSR can obtain better and more stable results in a wider range of the dimension of random subspace and the number of random subspaces. Extensive experimental results on the several UCI datasets and face image datasets have demonstrated the effectiveness of the proposed SSM-RSSR.