Taking the assumption that data samples are able to be reconstructed with the dictionary
formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a …

Multiple kernel clustering (MKC) optimally utilizes a group of pre-specified base kernels to
improve clustering performance. Among existing MKC algorithms, the recently proposed late …

Existing non-negative matrix factorization based multi-view clustering algorithms compute
multiple coefficient matrices respect to different data views, and learn a common consensus …

Multi-view clustering (MVC), which effectively fuses information from multiple views for better
performance, has received increasing attention. Most existing MVC methods assume that …

Incomplete multi-view clustering is an important research topic in multimedia where partial
data entries of one or more views are missing. Current subspace clustering approaches …

Kernel method is a proven technique in multi-view learning. It implicitly defines a Hilbert
space where samples can be linearly separated. Most kernel-based multi-view learning …

Kernel k-means (KKM) and spectral clustering (SC) are two basic methods used for multiple
kernel clustering (MKC), which have both been widely used to identify clusters that are non …

The multiple kernel-means (MKKM) and its variants utilize complementary information from
different sources, achieving better performance than kernel-means (KKM). However, the …

Most of existing nonnegative matrix factorization (NMF) methods do not fully exploit global
and local similarity information from data. In this paper, we propose a novel local similarity …

Current multiple kernel clustering algorithms compute a partition with the consensus kernel
or graph learned from the pre-specified ones, while the emerging late fusion methods firstly …