Geometric-inspired graph-based Incomplete Multi-view Clustering

Abstract

Multi-view clustering methods group data into different clusters by discovering the consensus in heterogeneous sources, which however becomes difficult when partial views of real-world data are missing. Consequently, reducing the impact of missing views and leveraging available views are the key concerns for the Incomplete Multi-view Clustering (IMvC) problem. In this research, we take an innovative, geometry-based perspective to investigate the IMvC problem under a commonly-used weight aggregation framework. We conduct a geometric analysis to understand how missing views shift the aggregation solution from the one achieved with full views, subsequently impacting the clustering result. Drawing from our analysis, we introduce a weight reallocation approach that minimizes the shift and approximates the full-view solution by reallocating the factual weight of each available view. Furthermore, we address the IMvC problem by using our reallocation method on a graph aggregation algorithm to obtain reliable clusters. Our extensive experiments demonstrate that our proposed approach outperforms previous IMvC methods, reporting superior results on four datasets with three metrics. Especially, on the Caltech101-7 dataset with 40 percent missing data, our method achieves an accuracy of 0.686, which significantly outperforms the results of other comparison methods that are no larger than 0.662. Further, our method can be used as a flexible plugin to improve other weight aggregation algorithms. The source code of this work is publicly available at https://github.com/bjlfzs/Geometric-Inspired-Graph-based-Incomplete-Multi-view-Clustering.