Multi-view Graph Learning by Joint Modeling of Consistency and Inconsistency

TL;DR

This paper introduces a novel multi-view graph learning framework that explicitly models both consistency and inconsistency across views, improving robustness and efficiency in multi-view clustering tasks.

Contribution

It is the first to simultaneously model multi-view consistency and inconsistency within a unified framework, with an efficient optimization algorithm for large-scale data.

Findings

Demonstrates robustness on twelve multi-view datasets.

Achieves linear time complexity in graph size.

Outperforms existing methods in clustering accuracy.

Abstract

Graph learning has emerged as a promising technique for multi-view clustering with its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency across multiple views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and multi-view inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm which is able to obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on twelve multi-view datasets have demonstrated the robustness and efficiency of the proposed approach.