Local linear neighbor reconstruction for multi-view data

Linlin Zong, Xianchao Zhang, Hong Yu, Qianli Zhao, Feng Ding

Abstract: Highlights•We propose to learn a unified similarity matrix by using multi-view information.•The unified similarity matrix interacts with multi-view original local data.•The learned similarity matrix enhances both spectral clustering and label propagation.•Results on several datasets show the effectiveness of the learned similarity matrix.