Cross-view hashing via supervised deep discrete matrix factorization

•We propose a discrete deep matrix factorization to learn unified hash codes which can capture more complex structure within heterogeneous data, resulting in more representative hashing codes.•We further introduce a linear classification error term to make the learned unified hashing codes discriminative.•We directly conduct discrete optimization which can reduce the quantization error.