One-step multi-view spectral clustering with cluster label correlation graph

Recently, one-step clustering methods have shown good performance. However, very few one-step approaches have addressed the multi-view case, where an instance may have multiple representations. Data can be represented with multiple heterogeneous views. Clustering with multiple views faces the challenge of how to combine all the different views. A general scheme is to represent the views by view-based graphs and/or a consensus graph. Graphs can be well suited for clustering problems since they can capture the local and global structure of the data. In this paper, we present a novel approach to one-step graph-based multi-view clustering. In contrast to existing graph-based one-step clustering methods, our proposed method introduces two key innovations. First, we build an additional graph by using the cluster label correlation to the graphs associated with the data space. Second, a smoothing constraint is exploited to constrain the cluster-label matrix and make it more consistent with the original data graphs as well as with and label graphs. Experimental results on several public datasets show the efficiency of the proposed approach. All cluster evaluation metrics show significant improvement by applying our method to different types and sizes of datasets. The average improvement (across all datasets) is the difference between the indicator obtained by our approach and the indicator obtained by the most competitive method. The average improvement is approximately 4%, 2%, 3%, and 2% for the Accuracy indicator, the Normalized Mutual Information indicator, the Purity indicator, and the Adjusted Rand index, respectively. (C) 2022 The Authors. Published by Elsevier Inc.