Semi-supervised feature selection analysis with structured multi-view sparse regularization.

•The structured multi-view sparse regularization is constructed.•Structured Multi-view Hessian sparse Feature Selection framework is proposed.•Multi-view Hessian regularization is utilized to enhance the performance.•A new iterative algorithm is introduced and its convergence is proven.•Experiments demonstrate SMHFS can effectively combine multi-view data information.