Adaptive graph weighting for multi-view dimensionality reduction

Multi-view learning has become a flourishing topic in recent years since it can discover various informative structures with respect to disparate statistical properties. However, multi-view data fusion remains challenging when exploring a proper way to find shared while complementary information. In this paper, we present an adaptive graph weighting scheme to conduct semi-supervised multi-view dimensional reduction. Particularly, we construct a Laplacian graph for each view, and thus the final graph is approximately regarded as a centroid of these single view graphs with different weights. Based on the learned graph, a simple yet effective linear regression function is employed to project data into a low-dimensional space. In addition, our proposed scheme can be well extended to an unsupervised version within a unified framework. Extensive experiments on varying benchmark datasets illustrate that our proposed scheme is superior to several state-of-the-art semi-supervised/unsupervised multi-view dimensionality reduction methods. Last but not least, we demonstrate that our proposed scheme provides a unified view to explain and understand a family of traditional schemes.