Discriminative multi-modality non-negative sparse graph model for action recognition

A discriminative multi-modality non-negative sparse (DMNS) graph model is proposed in this paper. In the model, features in each modality are first projected into the Mahalanobis space by a transformation learned for this modality, a multi-modality non-negative sparse graph is then constructed in the Mahalanobis space with shared coefficients across modalities. Both the labeled and unlabeled data can be introduced into the graph, and label propagation can then be performed to predict labels of the unlabeled samples. Extensive experiments over two benchmark datasets demonstrate the advantages of the proposed DMNS-graph method over the state-of-the-art methods.