Self-adaptive multi-view clustering algorithm with complementarity based on weighted anchors
In multi-view clustering problems,how to fully mine the correlation information among views while reducing the influence of redundant information on clustering performance is an urgent problem that needs to be solved.But the existing related algorithms ignore the complementary information and differences among views,and do not consider the interference brought by redundant information,resulting in poor clustering performance.To address these issues,a Self-adaptive Multi-view clustering algorithm with Complementarity based on Weighted Anchors(SMCWA)was proposed.When dealing with the challenges of high-dimensional multi-view data,firstly,feature concatenation was transferred to anchor mechanism,so as to fuse the anchor graphs to utilize the complementary information among views.Secondly,to weaken the expression of redundant information,the weight of each anchor was determined dynamically through a weighted matrix during the iteration process.Finally,to utilize the differences among views,an auto-weighted mechanism was used to assign appropriate weight to each view adaptively.The complementarity among views,the weakening of redundant information,and the differences among views promoted and learned from each other in multi-step iterations in an integrated algorithm to obtain better clustering effect.Experimental results show that the proposed algorithm improves Matthews Correlation Coefficient(MCC)by 41.75%on dataset BDGP(Berkeley Drosophila Genome Project)compared to spectral clustering algorithm SC-Concat,improves MCC by 11.83%on dataset CCV(Columbia Consumer Video)compared to Large-scale Multi-View Subspace Clustering in linear time(LMVSC)algorithm,and improves MCC by 19.57%on dataset Caltech101-all compared to the spectral clustering algorithm SC-Best,demonstrating that the proposed algorithm makes full consideration of the complementary information,the differences among views and the redundant information to obtain better clustering performance.
万方数据
维普
