INCOMPLETE MULTIPLE VIEW CLUSTERING BASED ON CONSISTENT GUIDANCE

In order to solve the problems of poor effect and weak generalization ability of traditional clustering methods,an incomplete multiple view clustering method based on consistent guidance is proposed.Graph learning and consistent representation learning were integrated into a joint framework to make full use of multiple view data information.The adaptive learning weight vector was introduced to balance the influence of different views,and the joint regularization representation learning strategy provided more freedom for consistent representation learning.An alternative iterative optimization algorithm was proposed to optimize the clustering.Experimental results on seven data sets show that the proposed method can effectively improve the effect of incomplete multiple view clustering.