Node-Adaptive Multi-Graph Fusion Using Extreme Value Theory

Abstract

This letter considers the problem of grouping data by their underlying categories with inputs from multiple sources, known as the multi-view clustering problem. One of the most fundamental challenges lies in how to benefit from the complementary information in the multi-view data, so that clustering on such data consistently achieves higher accuracy than clustering on each single-view component. In this letter, to tackle the multi-view clustering problem, we propose a novel approach to fuse multiple affinity graphs computed in each single view to a unified affinity graph, so that single-view affinity-based clustering methods can be accordingly applied on it. The edges in the unified affinity graph between a node and its neighbors are computed as weighted average over the corresponding edges from multiple single graphs, and the weights here are adaptive to each node, estimated using the Extreme Value Theory (EVT). Experiments on two challenging multi-view clustering tasks show that, combined with existing off-the-shelf single-view clustering algorithms, the proposed graph fusion method brings consistently performance gain compared with naive graph fusion baselines.