Incomplete multi-view clustering (IMVC) analysis, where some views of multi-view data
usually have missing data, has attracted increasing attention. However, existing IMVC …

Multiview clustering partitions data into different groups according to their heterogeneous
features. Most existing methods degenerate the applicability of models due to their …

A graph is usually formed to reveal the relationship between data points and graph structure
is encoded by the affinity matrix. Most graph-based multiview clustering methods use …

In the big data era, the data are generated from different sources or observed from different
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …

Incomplete multiview clustering (IMC) is a hot and emerging topic. It is well known that
unavoidable data incompleteness greatly weakens the effective information of multiview …

Incomplete multi-view clustering, which aims to solve the clustering problem on the
incomplete multi-view data with partial view missing, has received more and more attention …

In this paper, we propose a novel method, referred to as incomplete multi-view tensor
spectral clustering with missing-view inferring (IMVTSC-MVI) to address the challenging …

Most existing graph-based clustering methods need a predefined graph and their clustering
performance highly depends on the quality of the graph. Aiming to improve the multiview …

Multi-view subspace clustering targets at clustering data lying in a union of low-dimensional
subspaces. Generally, an n X n affinity graph is constructed, on which spectral clustering is …

Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achieved
impressive performance due to the powerful representation extracted using deep neural …