Topological-aware Multiple Kernel Learning for Ensemble Clustering

Ensemble clustering integrates a set of base clustering results to obtain more accurate consensus results. Most existing methods transform base clustering results into a co-association (CA) matrix, which describes the frequency with which samples belong to the same cluster. However, these methods still exhibit insufficient learning of sample similarity relationships, overlooking the capture of a more comprehensive similarity relationship from multiple perspectives. To address this issue, we propose a method called tOpological-aware Multiple kErnel Learning for Ensemble clusTering (OMELET). Specifically, OMELET uses multiple kernel functions to remeasure the similarity between samples from multiple perspectives. To fully exploit the sample similarity relationships in different kernel spaces, OMELET employs multiple kernel learning to learn a consensus kernel and obtain the similarity matrix between samples. To eliminate noise from the similarity matrix captured in the fused kernel space, OMELET leverages topological similarity propagated among highly similar neighbors to reshape the similarity graph. Finally, OMELET captures a topological affinity matrix that represents the sample similarity relationships. Moreover, to enable OMELET to scale on large datasets, we derive a variant called OMELET-SV, which leverages anchors to construct an anchor-based kernel matrix, thereby improving computational efficiency. Extensive experimental results demonstrate the outstanding performance of OMELET compared to 18 representative methods.