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 …

Multiple kernel clustering (MKC) has garnered considerable attention in recent years, aiming
to obtain an optimal partition from multiple base kernels. Existing MKC methods typically …

Multiple Kernel Clustering (MKC) has emerged as a prominent research domain in recent
decades due to its capacity to exploit diverse information from multiple views by learning an …

In this paper we propose a novel concept factorization method that seeks factor matrices
using a cross-order positive semi-definite neighbor graph which provides comprehensive …

Multiple kernel k-means clustering (MKKC) is proposed to efficiently incorporate multiple
base kernels to generate an optimal kernel. However, many existing MKKC methods all …

With its simplicity and effectiveness, k-means is immensely popular, but it cannot perform
well on complex nonlinear datasets. Multiple kernel k-means (MKKM) demonstrates the …

Spectral clustering (SC) is a well-performed and prevalent technique for data processing
and analysis, which has attracted significant attention in the field of clustering. While the …

Multiple kernel clustering (MKC) enhances clustering performance by deriving a consensus
partition or graph from a predefined set of kernels. Despite many advanced MKC methods …

In recent years, outliers caused by manual operation errors and equipment acquisition
failures often occur, bringing challenges to big data analysis. In view of the difficulties in …

Clustering, as a classical unsupervised artificial intelligence technology, is commonly
employed for hyperspectral image clustering tasks. However, most existing clustering …