Efficient Multi-view K-means Clustering with Multiple Anchor Graphs

Multi-view clustering has attracted a lot of attention due to its ability to integrate information from distinct views, but how to improve efficiency is still a hot research topic. Anchor graph-based methods and k-means-based methods are two current popular efficient methods, however, both have limitations. Clustering on the derived anchor graph takes a while for anchor graph-based methods, and the efficiency of k-means-based methods drops significantly when the data dimension is large. To emphasize these issues, we developed an efficient multi-view k-means clustering method with multiple anchor graphs (EMKMC). It first constructs anchor graphs for each view and then integrates these anchor graphs using an improved k-means strategy to obtain sample categories without any extra post-processing. Since EMKMC combines the high-efficiency portions of anchor graph-based methods and k-means-based methods, its efficiency is substantially higher than current fast methods, especially when dealing with large-scale high-dimensional multi-view data. Extensive experiments demonstrate that, compared to other state-of-the-art methods, EMKMC can boost clustering efficiency by several to thousands of times while maintaining comparable or even exceeding clustering effectiveness.