Low-rank regularized tensor discriminant representation for image set classification.

•A general framework for learning discriminant representations of samples is proposed to better solve the classification of image sets.•Under the low-rank and the Grassmann assumptions, a latent space is obtained to capture the lowest-rank representations of samples.•An alternating direction algorithm based on ISTA is proposed to better ensure the convergence of the proposed model.