Multi-view graph learning with adaptive label propagation

Abstract

Graphs play an essential role in many data mining paradigms, such as semi-supervised classification. Conventional graph learning methods mainly focus on constructing graphs from single-view data. Nowadays data can be collected from multiple views using various sensors. How to construct a robust and reliable graph from multi-view data is still an open problem. In this paper, we propose a multi-view graph learning (MVGL) approach with adaptive label propagation for semi-supervised classification. MVGL integrates latent factor extraction, graph sparsification, and label propagation into a unified framework. It seeks shared latent factors from multi-view data as view-independent data representations, and then constructs a sparse graph accordingly. Meanwhile, the label propagation is adaptively optimized during graph construction. An efficient optimization algorithm is designed to solve the model. Experimental results on two benchmark datasets show remarkable improvements over both single-view and multi-view learning baselines.