Adaptive-weighting discriminative regression for multi-view classification.

•We address the multi-view feature learning problem with a novel discriminative regression based framework, which maps the multi-view data to a unified low-dimensional discriminative subspace.•We introduce a set of learnable weight parameters that can be merged into the transformation matrix, such that the correlative and the complementary information of the original views can be preserved in the projected subspace simultaneously.•We design an efficient iterative optimization algorithm with closed-form solution to update the learnable parameters during each iteration, which expresses a remarkable convergence speed in extensive experiments.