Projected cross-view learning for unbalanced incomplete multi-view clustering

Yiran Cai, Hangjun Che, Baicheng Pan, Man-Fai Leung, Cheng Liu, Shiping Wen

Abstract: Highlights•The paper introduces a projected cross-view learning method for unbalanced incomplete multi-view clustering.•Reconstruction terms of different dimensions are constructed to handle the unbalanced incomplete multi-view data by completing the missing samples.•A projection matrix is incorporated into the model to reduce the impact of information imbalance.•A graph regularization term is integrated to preserve the original latent spatial structure of the data.