One-step Multi-view Clustering Based on Diversity and Consistency
With the development of data collection technology,multi-view data have become increasingly common.Compared to single-view data,multi-view data contain richer information,which is usually characterized by consistency and diversity information.Most multi-view clustering methods based on graphs focus only on consistency information,neglect diversity information,and separate the construction of graphs from the clustering process,which may affect the clustering algorithm performance.This study proposes a One-step Multi-view Clustering algorithm based on Diversity and Consistency(OMCDC).It first constructs similarity graphs for each view based on the prior knowledge of"data points with smaller distances are more likely to become neighbors."Second,unlike previous algorithms that directly fuse similarity graphs to obtain a common graph,this study decomposes the similarity graphs of each view into consistency and diversity graphs,and thereafter obtains a more consistent common graph by fusing the consistency graphs.Furthermore,spectral rotation is introduced to jointly optimize the low-dimensional spectral embedding and clustering probability matrix,integrating graph learning and clustering to obtain the clustering results directly.OMCDC fully uses the consistency and diversity information of multi-view data and combines spectral rotation to achieve one-step multi-view clustering.The clustering accuracies of this method on the 100Leaves(100L)and HandWritten digits2(HW2)datasets are 94.62％and 99.30％,respectively.Compared with Graph Learning for Multi-View clustering(MVGL),multi-view clustering via Adaptively Weighted Procrustes(AWP),and Multi-view Consensus Graph Clustering(MCGC),OMCDC achieves better clustering performance.
国家科技期刊平台
NSTL
万方数据
