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

Existing multi-kernel clustering (MKC) methods usually focus on constructing a fixed
dimension consensus-partition from base kernels to demonstrate their superior in integrating …

Consistency plays an important role in learning theory. However, in multiple kernel
clustering (MKC), the consistency of kernel weights has not been sufficiently investigated. In …

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 …

Graph learning can effectively characterize the similarity structure of sample pairs, hence
multiple kernel clustering based on graph learning (MKC-GL) achieves promising results on …

Multiple kernel clustering (MKC) has demonstrated promising performance for handing non-
linear data clustering. Positively, it can integrate complementary information of multiple base …

Owing to its simplicity, the traditional-means (Lloyd heuristic) clustering method plays a vital
role in a variety of machine-learning applications. Disappointingly, the Lloyd heuristic is …

Multiple Kernel k-means and its variants integrate a group of kernels to improve clustering
performance, but it still has some drawbacks: 1) linearly combining base kernels to get the …

Multiview clustering (MVC) has been widely studied in machine learning and data mining for
its capability of improving clustering performance by fusing the information from multiview …