In recent years, deep clustering has achieved encouraging results. However, existing deep
clustering methods work with the traditional Euclidean space and thus present deficiency on …

Incomplete multiview clustering (IMVC) has received extensive attention in recent years.
However, existing works still have several shortcomings: 1) some works ignore the …

Optimal transport (OT) is an effective tool for measuring discrepancies in probability
distributions and histograms of features. To reduce its high computational complexity …

This article presents a general framework for the transport of probability measures towards
minimum divergence generative modeling and sampling using ordinary differential …

We study decision problems under uncertainty, where the decision-maker has access to $ K
$ data sources that carry {\em biased} information about the underlying risk factors. The …

With the rapid advancement of generative models, image detectors for AI-generated content
have become an increasingly necessary technology in computer vision, attracting significant …

Wasserstein dictionary learning is an unsupervised approach to learning a collection of
probability distributions that generate observed distributions as Wasserstein barycentric …

Optimal Transport has received much attention in Machine Learning as it allows to compare
probability distributions by exploiting the geometry of the underlying space. However, in its …

In the process of the K-means clustering algorithm, one of the issues that arises is the high
number of iterations. This study aims to optimize the cluster evaluation results in K-means by …

We develop a novel clustering method for distributional data, where each data point is
regarded as a probability distribution on the real line. For distributional data, it has been …