Adaptive graph learning algorithm for incomplete multi-view clustered image segmentation

Abstract

There are problems of relying on data initialization and ignoring data structure in existing incomplete multi-view clustering algorithms, an adaptive graph learning incomplete multi-view clustering image segmentation algorithm is proposed. Firstly, the similarity matrix of each non missing view is adaptive learned, and the index matrix of the missing view is used to complete the similarity matrix and unify the dimensions,which ensure the authenticity of the data and revealing the data structure. Secondly, the low dimension representation of the complete similarity matrix under spectral constraints is calculated, and a discrete clustering index matrix is directly obtained through adaptive weighted spectral rotation, avoiding post-processing. The clustering index matrix is used to obtain clustering of multi-view features, thereby obtaining image segmentation results. Finally, an iterative algorithm optimization model is presented, which is compared with six existing algorithms using seven evaluation metrics on six datasets. The results show significant improvements in clustering performance and segmentation performance.