Multi-view reduced dimensionality K-means clustering with σ-norm and Schatten p-norm

Recently, multi-view high dimensional data obtained from diverse domains or various feature extractors has drawn great attention due to its reflection of different properties or distributions. In this paper, we propose a novel unsupervised multi-view clustering method, which is called Multi-View Reduced Dimensionality K-means clustering (MRDKM) and integrates the dimension reduction mechanism, sigma-norm, Schatten p-norm, and multi-view K-means clustering. Moreover, an unsupervised optimization scheme was proposed to solve the minimization problem with good convergence properties. Comprehensive evaluations of five benchmark datasets and comparisons with several multi-view clustering algorithms demonstrate the superiority of the proposed work.