Convex Clustering Redefined: Robust Learning with the Median of Means Estimator

• URL: http://arxiv.org/abs/2511.14784v1
• Date: Wed, 12 Nov 2025 21:16:53 GMT
• Title: Convex Clustering Redefined: Robust Learning with the Median of Means Estimator
• Authors: Sourav De, Koustav Chowdhury, Bibhabasu Mandal, Sagar Ghosh, Swagatam Das, Debolina Paul, Saptarshi Chakraborty,
• Abstract summary: We introduce a robust approach that integrates convex clustering with the Median of Means (MoM) estimator.<n>Our method enhances both performance and efficiency, especially on large-scale datasets.
• Score: 22.614296433333106
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clustering approaches that utilize convex loss functions have recently attracted growing interest in the formation of compact data clusters. Although classical methods like k-means and its wide family of variants are still widely used, all of them require the number of clusters k to be supplied as input, and many are notably sensitive to initialization. Convex clustering provides a more stable alternative by formulating the clustering task as a convex optimization problem, ensuring a unique global solution. However, it faces challenges in handling high-dimensional data, especially in the presence of noise and outliers. Additionally, strong fusion regularization, controlled by the tuning parameter, can hinder effective cluster formation within a convex clustering framework. To overcome these challenges, we introduce a robust approach that integrates convex clustering with the Median of Means (MoM) estimator, thus developing an outlier-resistant and efficient clustering framework that does not necessitate prior knowledge of the number of clusters. By leveraging the robustness of MoM alongside the stability of convex clustering, our method enhances both performance and efficiency, especially on large-scale datasets. Theoretical analysis demonstrates weak consistency under specific conditions, while experiments on synthetic and real-world datasets validate the method's superior performance compared to existing approaches.

Related papers

• Sparse Convex Biclustering [3.067019303674385]
  Biclustering robustness is an essential machine learning technique for simultaneously clustering rows and columns of a data matrix.<n>We propose a novel method that penalizes noise during the biclustering process to improve both accuracy and stability.
  arXiv  Detail & Related papers  (2026-01-05T03:15:52Z)
• You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering [73.48306836608124]
  DCBoost is a parameter-free plug-in designed to enhance the global feature structures of current deep clustering models.<n>By harnessing reliable local structural cues, our method aims to elevate clustering performance effectively.
  arXiv  Detail & Related papers  (2025-11-26T09:16:36Z)
• K*-Means: A Parameter-free Clustering Algorithm [55.20132267309382]
  k*-means is a novel clustering algorithm that eliminates the need to set k or any other parameters.<n>It uses the minimum description length principle to automatically determine the optimal number of clusters, k*, by splitting and merging clusters.<n>We prove that k*-means is guaranteed to converge and demonstrate experimentally that it significantly outperforms existing methods in scenarios where k is unknown.
  arXiv  Detail & Related papers  (2025-05-17T08:41:07Z)
• Self-Supervised Graph Embedding Clustering [70.36328717683297]
  K-means one-step dimensionality reduction clustering method has made some progress in addressing the curse of dimensionality in clustering tasks. We propose a unified framework that integrates manifold learning with K-means, resulting in the self-supervised graph embedding framework.
  arXiv  Detail & Related papers  (2024-09-24T08:59:51Z)
• GCC: Generative Calibration Clustering [55.44944397168619]
  We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure. First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples. Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
  arXiv  Detail & Related papers  (2024-04-14T01:51:11Z)
• Fuzzy K-Means Clustering without Cluster Centroids [21.256564324236333]
  Fuzzy K-Means clustering is a critical technique in unsupervised data analysis. This paper proposes a novel Fuzzy textitK-Means clustering algorithm that entirely eliminates the reliance on cluster centroids.
  arXiv  Detail & Related papers  (2024-04-07T12:25:03Z)
• Deep Embedding Clustering Driven by Sample Stability [16.53706617383543]
  We propose a deep embedding clustering algorithm driven by sample stability (DECS) Specifically, we start by constructing the initial feature space with an autoencoder and then learn the cluster-oriented embedding feature constrained by sample stability. The experimental results on five datasets illustrate that the proposed method achieves superior performance compared to state-of-the-art clustering approaches.
  arXiv  Detail & Related papers  (2024-01-29T09:19:49Z)
• A provable initialization and robust clustering method for general mixture models [6.806940901668607]
  Clustering is a fundamental tool in statistical machine learning in the presence of heterogeneous data. Most recent results focus on optimal mislabeling guarantees when data are distributed around centroids with sub-Gaussian errors.
  arXiv  Detail & Related papers  (2024-01-10T22:56:44Z)
• Dirichlet Process-based Robust Clustering using the Median-of-Means Estimator [16.774378814288806]
  We propose an efficient and automatic clustering technique by integrating the strengths of model-based and centroid-based methodologies.<n>Our method mitigates the effect of noise on the quality of clustering; while at the same time, estimates the number of clusters.
  arXiv  Detail & Related papers  (2023-11-26T19:01:15Z)
• Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation [65.268245109828]
  Online deep clustering refers to the joint use of a feature extraction network and a clustering model to assign cluster labels to each new data point or batch as it is processed. While faster and more versatile than offline methods, online clustering can easily reach the collapsed solution where the encoder maps all inputs to the same point and all are put into a single cluster. We propose a method that does not require data augmentation, and that, differently from existing methods, regularizes the hard assignments.
  arXiv  Detail & Related papers  (2023-03-29T08:23:26Z)
• Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
  Existing scalable hierarchical clustering methods sacrifice quality for speed. We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
  arXiv  Detail & Related papers  (2020-10-22T15:58:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.