Multi-view clustering has received widespread attention owing to its effectiveness by
integrating multi-view data appropriately, but traditional algorithms have limited applicability …

Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known
method for handling high-dimensional multi-view data. To satisfy the non-negativity …

Multi-view clustering has gained great progress recently, which employs the representations
from different views for improving the final performance. In this paper, we focus on the …

Nonnegative matrix factorization (NMF) based multiview technique has been commonly
used in multiview data clustering tasks. However, previous NMF based multiview clustering …

Graph-regularized non-negative matrix factorization (GNMF) is proved to be effective for the
clustering of nonlinear separable data. Existing GNMF variants commonly improve model …

Symmetric NMF (SNMF) is able to determine the inherent cluster structure with the
constructed graph. However, the mapping between the empirically constructed similarity …

Labeling expensive and graph structure fuzziness are recognized as indispensable
prerequisites for solving practical problems in semi-supervised graph learning. This paper …

Kernelized concept factorization (KCF) has shown its advantage on handling data with
nonlinear structures; however, the kernels involved in the existing KCF-based methods are …

Considering many graph clustering methods are with quadratic or cubic time complexity and
need post-processing to obtain the discrete solution. Combining with the anchor graph, we …

This article explores the problem of semisupervised affinity matrix learning, that is, learning
an affinity matrix of data samples under the supervision of a small number of pairwise …