Adaptive Anchor-Based Partial Multiview Clustering

Abstract

Partial multiview clustering, which aims to effectively merge multiple prespecified incomplete views to improve clustering performance, is a research hotspot and difficulty in the field of machine learning. Guo et al. proposed a partial multiview clustering method (APMC) based on anchor graph, which uses a Gauss kernel function to solve the similarity matrix. The Gaussian kernel function is sensitive to parameter σ, and it is difficult to find the optimal value only by stepwise adjustment in practical applications. This undoubtedly affects the practicality of the APMC algorithm. To address this issue, an adaptive partial multiview clustering method based on anchor graph (AAPMC) is developed in this paper, which proposes an adaptive neighbor assignment strategy and utilizes it to improve the anchor-based similarity matrix computation of each view. The method proposed in this paper uses an adaptive method to solve the similarity matrix, eliminating the tediousness of parameter adjustment. In addition, the non-iterative method based on anchors is used to solve the optimal solution with low time complexity and is suitable for large-scale datasets. In short, our method is simple and effective, and it is easier to implement in practice. Extensive experiments show that our model can not only effectively solve the parameter setting problem, but also performs better than the state-of-the-art partial multiview clustering methods.