Low-Rank Regularized Deep Collaborative Matrix Factorization for Micro-Video Multi-Label Classification

Abstract

Deep matrix factorization can be regarded as an extension of traditional matrix factorization to help improve applications like social image tag refinement, image retrieval, and face clustering. Toward this tendency, in this letter, we proposed a low-rank regularized deep collaborative matrix factorization (LRDCMF) method to better tackle micro-video multi-label classification tasks. The proposed method aims to collaboratively learn two sets of factor matrices for characterization of latent attributes and two deep representations for instances and labels, respectively. During factorization process, the inverse covariance constraints are exploited to capture the latent correlation structures among latent attributes and labels and the low-dimensional intrinsic deep representations are ensured by further considering low-rank constraints. Moreover, a triplet term that connects instances representations, label representations, and labels is constructed to increase discrimination power of our method. Experimental results conducted on a large-scale micro-video dataset illustrate our model achieves superior performance in comparison with state-of-the-art methods.