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 …

The kernel k-means method usually loses its power when clustering high-dimensional data,
due to a large number of irrelevant features. We propose a novel sparse kernel k-means …

Kernel-means clustering is a powerful tool for unsupervised learning of non-linearly
separable data. Its merits are thoroughly validated on a suite of simulated datasets and real …

Mean shift is a simple interactive procedure that gradually shifts data points towards the
mode which denotes the highest density of data points in the region. Mean shift algorithms …

Clustering has become one of the widely used automatic data-labeling techniques applied
in a variety of disciplines. Kernel-based clustering is a technique designed to identify non …

Existing multiple kernel clustering (MKC) algorithms have two ubiquitous problems. From the
theoretical perspective, most MKC algorithms lack sufficient theoretical analysis, especially …

We provide uniform concentration bounds on the kernel k-means clustering objective based
on Rademacher complexity by posing the underlying problem as a risk minimization task …

In unsupervised learning, clustering is a common starting point for data processing. The
convex or concave fusion clustering method is a novel approach that is more stable and …

Using random projection, a method to speed up both kernel k-means and centroid
initialization with k-means++ is proposed. We approximate the kernel matrix and distances …

The segmentation and clustering of 3D point clouds play a pivotal role in the navigation of
mobile robots. In this study, algorithms for segmenting and clustering 3D point clouds were …