基于加权锚点的多视图聚类算法OA北大核心CSTPCD

Large-scale multi-view clustering aims to solve the problem that traditional methods cannot scale to large-scale data due to slow computational speed and high complexity.Among them,the anchor-based multi-view clustering method constructs a reconstruction matrix for the entire dataset by utilizing a set of anchor points.Clus-tering with the reconstruction matrix effectively reduces the time and space complexity of the algorithm.However,existing methods ignore the differences among anchor points and treat them equally,resulting in clustering results limited by low-quality anchor points.In order to identify more discriminative anchor points and enhance the influ-ence of high-quality anchors on clustering,a large-scale multi-view clustering algorithm based on weighted anchors(MVC-WA)was proposed.By introducing an adaptive anchor weighting mechanism,the proposed method determ-ine the weights of anchors in a unified framework for the construction of anchor graphs.Meanwhile,in order to in-crease the diversity among anchors,the weights of anchors were further adjusted according to the similarity between them.Experimental results comparing with existing state-of-the-art large-scale multi-view clustering al-gorithms on nine benchmark datasets validate the efficiency and effectiveness of the proposed method.