How to Construct Corresponding Anchors for Incomplete Multiview Clustering

Anchor based incomplete multiview clustering has grasped growing interest recently because of its great success in effectively partitioning multimodal data. However, due to the absence of label information, the constructed anchors could be mismatched. Such an Anchor Mismatching Problem (AMP) will cause the structure of generated bipartite graph to be chaotic, degrading the clustering performance. To tackle this issue, we design an algorithm termed Constructing Corresponding Anchors for Incomplete Multiview Clustering (CCA-IMC). Specifically, we first devise a permutation strategy to transform anchors on each view. Subsequently, we directly generate the consensus bipartite graph, which is shared for all incomplete views, by the transformed anchors rather than by fusing each view-specific bipartite graph. Afterwards, all anchors and permutation matrices as well as the consensus bipartite graph are jointly optimized in one common framework so as to promote each other. In such ways, anchors are rearranged towards correct matching relationship according to the consensus graph structure. In addition to these, our CCA-IMC has also been proven to be with linear time and memory overheads, which makes it able to scale up to work with large-scale tasks. Massive experiments implemented on ten popular datasets give evidence of our superiorities compared to current strong IMC competitors.