Multi-view constrained clustering with an incomplete mapping between views

TL;DR

This paper introduces a multi-view constrained clustering algorithm capable of handling incomplete view mappings by propagating constraints within views and transferring them across views, improving clustering accuracy with limited view correspondence.

Contribution

The proposed method enables multi-view clustering with incomplete mappings by propagating and transferring constraints, a novel approach for partial view correspondence scenarios.

Findings

Significantly improves clustering performance over existing methods.

Propagated constraints exhibit high precision in true clusters.

Effective in both single- and multi-view learning scenarios.

Abstract

Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via the partial mapping. It uses co-EM to iteratively estimate the propagation within each view based on the current clustering model, transfer the constraints across views, and then update the clustering model. By alternating the learning process between views, this approach produces a unified clustering model that is consistent with all views. We show that this approach significantly improves clustering performance over several other methods for transferring constraints and allows multi-view clustering to be reliably applied when given a limited mapping between the views. Our evaluation reveals that the propagated constraints have high precision with respect to the true clusters in the data, explaining their benefit to clustering performance in both single- and multi-view learning scenarios.