arXiv Statistics ML August 31, 2026 By Babak F. Dehkordi, Jeffrey L. Andrews, Andrew Jirasek

Robust model-based clustering via mixtures of multivariate pseudo-Voigt distributions

Read the original on arXiv Statistics ML →

The paper introduces a multivariate pseudo‑Voigt mixture model, combining Gaussian and Cauchy components with shared location and scale parameters, for robust clustering and outlier detection. Parameter estimation is performed using an EM algorithm that leverages latent variables for efficient likelihood inference. The authors evaluate the model through simulations and real data, comparing it to established robust mixtures such as contaminated normals, and demonstrate its effectiveness on heavy‑tailed datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

arXiv Machine Learning

Sep 3

Clustering Three-Way Data with Outliers

The paper introduces a method for clustering matrix-variate normal data that accounts for outliers. It extends the OCLUST algorithm by employing subset log-likelihood distributions and an iterative trimming procedure. This approach enables robust clustering of complex structured data such as images and time series.

By Katharine M. Clark, Paul D. McNicholas

arXiv Statistics ML

Sep 1

Robust K-means Clustering using the Density Power Divergence Measure

arXiv:2608.30093v1 Announce Type: cross Abstract: We introduce a robust clustering method, MK-means DPD, that estimates cluster centers and covariance matrices using density power divergence (DPD) me...

By Anirban Mondal, Paromita Banerjee, Abhijit Mandal

arXiv Machine Learning

Jun 2

Cellwise and Casewise Robust Covariance in High Dimensions

arXiv:2505. 19925v2 Announce Type: replace-cross Abstract: The sample covariance matrix is a cornerstone of multivariate statistics, but it is highly sensitive to outliers.

By Fabio Centofanti, Mia Hubert, Peter J. Rousseeuw

arXiv Machine Learning

Aug 14

Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning

arXiv:2608. 13418v1 Announce Type: cross Abstract: Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution.

By Yikai Xu, Zhao Chen, Jian Huang

diffusion benchmarks

arXiv Machine Learning

Jul 31

A novel k-means clustering approach using two distance measures for Gaussian data

arXiv:2511. 17823v2 Announce Type: replace Abstract: Clustering algorithms have long been the topic of research, representing the more popular side of unsupervised learning.

By Naitik Gada (Rochester Institute of Technology)

benchmarks

arXiv Statistics ML

Sep 2

Deep Skew-t Mixture Models

arXiv:2609.00773v1 Announce Type: cross Abstract: High-dimensional clustering is challenging when component distributions are both heavy-tailed and directionally asymmetric. We propose a deep skew-$t...

By Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan

benchmarks

One email a morning, machine-written

Related stories

Clustering Three-Way Data with Outliers

Robust K-means Clustering using the Density Power Divergence Measure

Cellwise and Casewise Robust Covariance in High Dimensions

Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning

A novel k-means clustering approach using two distance measures for Gaussian data

Deep Skew-t Mixture Models