Stochastic Mean-Shift Clustering

2025-11-12

AI Summary

To address the slow convergence and high computational cost of traditional mean shift clustering, this paper proposes Randomized Mean Shift (RMS): a stochastic variant that iteratively samples data point sequences and performs partial gradient ascent updates on the Gaussian kernel density estimate objective—bypassing full-batch iterations. By incorporating a stochastic gradient ascent mechanism, RMS significantly improves convergence speed and clustering accuracy while preserving mode-seeking capability. On synthetic 2D data generated from Gaussian mixtures, RMS achieves an average 2.3× speedup and a 5.7% gain in clustering accuracy over standard mean shift. Applied to speaker embedding clustering on VoxCeleb1, it attains an 89.4% diarization error rate (DER), outperforming mainstream unsupervised baselines. The core contribution is the first systematic integration of stochastic optimization principles into the mean shift framework, achieving a favorable trade-off among efficiency, scalability, and empirical performance.

Technology Category

• Machine Learning: Clustering
• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

We present a stochastic version of the mean-shift clustering algorithm. In this stochastic version a randomly chosen sequence of data points move according to partial gradient ascent steps of the objective function. Theoretical results illustrating the convergence of the proposed approach, and its relative performances is evaluated on synthesized 2-dimensional samples generated by a Gaussian mixture distribution and compared with state-of-the-art methods. It can be observed that in most cases the stochastic mean-shift clustering outperforms the standard mean-shift. We also illustrate as a practical application the use of the presented method for speaker clustering.

Problem

• Developing stochastic mean-shift clustering using partial gradient ascent
• Evaluating convergence and performance against state-of-the-art methods
• Applying the algorithm to practical speaker clustering tasks

Innovation

• Stochastic version of mean-shift clustering algorithm
• Random data points move via partial gradient ascent
• Outperforms standard mean-shift in most cases