Fast correntropy-based multi-view clustering with prototype graph factorization

Abstract

As a consequence of the ability to incorporate information from different perspectives, multi-view clustering has gained significant attention. Nevertheless, 1) its high computational cost, particularly when processing large-scale and high-dimensional multi-view data, restricts its applications in practice; and 2) complex noise in real-world data also challenges the robustness of existing algorithms. To tackle the above challenges, we develop a fast correntropy-based multi-view clustering algorithm with prototype graph factorization (FCMCPF). FCMCPF first adopts prototype graphs to effectively mitigate the complexity associated with graph construction, thereby reducing it from a quadratic complexity to a linear one. Then, it decomposes these prototype graphs under the correntropy criterion to robustly find the cluster indicator matrix without any post-processing. To solve the non-convex and non-linear model, we devise a fast half-quadratic-based strategy to first convert it into a convex formulation and then swiftly complete the optimization via the matrix properties of orthogonality and trace. The extensive experiments conducted on noisy and real-world datasets illustrate that FCMCPF is highly efficient and robust compared to other advanced algorithms, with comparable or even superior clustering effectiveness.