Directly solving normalized cut for multi-view data

•The new method learns a set of implicit weights for each view to identify its quality, and the view weights can be adjusted by an additional parameter for better results.•A parameter p is introduced to adjust the distribution of these view weights to obtain better results.•An iterative algorithm with linear time complexity is proposed to directly optimize the new model without eigen-decomposition and post-processing.