Auto-weighted multi-view clustering via deep matrix decomposition.

•Anovel deep multi-view learning model is proposed by uncovering the hierarchical semantics of the input data in a layer-wise way.•The instances from the same class but from different views are forced to be closer layer by layer in the low-dimensional space, which is beneficial for the subsequent learning task.•To automatically determine the weights of different views, we introduce the auto-weighting scheme into the deep multi-view clustering algorithm.•To solve the optimization problem of our model, an efficient iterative updating algorithm is proposed with a theoretical guarantee of its convergence.