Implicit Weight Learning for Multi-View Clustering

Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering. In general, it is essential to measure the importance of each individual view, due to some noises, or inherent capacities in the description. Many previous works model the view importance as weight, which is simple but effective empirically. In this article, instead of following the traditional thoughts, we propose a new weight learning paradigm in the context of multi-view clustering in virtue of the idea of the reweighted approach, and we theoretically analyze its working mechanism. Meanwhile, as a carefully achieved example, all of the views are connected by exploring a unified Laplacian rank constrained graph, which will be a representative method to compare with other weight learning approaches in experiments. Furthermore, the proposed weight learning strategy is much suitable for multi-view data, and it can be naturally integrated with many existing clustering learners. According to the numerical experiments, the proposed implicit weight learning approach is proven effective and practical to use in multi-view clustering.

Automated summary

This article proposes a new weight learning paradigm for multi-view clustering and theoretically analyzes its working mechanism. The proposed approach connects all views through a unified Laplacian rank constrained graph as a carefully achieved example. The research results demonstrate that this implicit weight learning approach is effective and practical to use in multi-view clustering.