Cross-domain action recognition via collective matrix factorization with graph Laplacian regularization

This paper investigates the problem of cross-domain action recognition. Specifically, we present a cross-domain action recognition framework by utilizing some labeled data from other data sets as the auxiliary source domain. It is a challenging task as data from different domains may have different feature distribution. To map data from different domains into the same abstract space and boost the action recognition performance, we propose a method named collective matrix factorization with graph Laplacian regularization (CMFGLR). Our approach is built upon the technique of collective matrix factorization, which simultaneously learns a common latent space, linear projection matrices for obtaining semantic representations, and an optimal linear classifier. Moreover, we explore the label consistency across different domain and the local geometric consistency in each domain and obtain a graph Laplacian regularization term to enhance the discrimination of learned features. Experimental results verify that CMFGLR significantly outperforms several state-of-the-art methods. We apply collective matrix factorization to cross-domain action recognition.We build a novel graph Laplacian regularization term.The framework jointly learns semantic representations and a linear classifier.