Weight Adaptive Multi-view Spectral Clustering Algorithm Based on Consistent Graphs
With the popularity of mobile devices and the Internet,it has become easier to collect and share multi-view data.Multi-view data can describe data from multiple views more accurately.Currently,some multi-view clustering algorithms ignore consistent latent knowledge among different views and the importance of different views.To solve this problem,this paper proposes a multi-view clustering algorithm that balances the consistency information among different views.The proposed algorithm first learns the consistent shared similarity matrix among views by adjusting the weight of the views to improve the consistency of the shared matrix.Among them,views with strong correlation have more consistent information,and the weight of views is larger,which plays a greater role in consistency learning;in contrast,views with large differences have smaller weights and play a smaller role in learning.Moreover,the proposed algorithm learns the consistent sample embeddings of different views and the feature embeddings of different views,promoting the consistent expression of sample embeddings by transferring the diversity feature information contained in the feature embeddings to the sample embeddings.The features of the different views can complement the simple sample relationship in the shared similarity matrix learning described above.Therefore,this study used bipartite graph co-clustering to learn feature embeddings and transfer them to sample embeddings by building a relationship graph of the sample,sample embeddings,and feature embeddings.Finally,graph learning,spectral clustering,and feature embedding learning are effectively integrated into a unified framework for joint optimization.The experimental results show that the algorithm clusters sample embeddings using K-means,runs it on five real databases,and compares it with seven clustering algorithms.The correct rates of the 3-Sources,Yale,and MRSCV1 datasets are higher than those of comparison algorithms by more than 5%,which validates the effectiveness of this algorithm.
国家科技期刊平台
NSTL
万方数据
维普
