Doubly Stochastic Mean-Shift Clustering
arXiv:2602.15393v1 Announce Type: new Abstract: Standard Mean-Shift algorithms are notoriously sensitive to the bandwidth hyperparameter, particularly in data-scarce regimes where fixed-scale density estimation leads to fragmentation and spurious modes. In this paper, we propose Doubly Stochastic Mean-Shift (DSMS), a novel extension that introduces randomness not only in the trajectory updates but also in the kernel bandwidth itself. By drawing both the data samples and the radius from a continuous uniform distribution at each iteration, DSMS effectively performs a better exploration of the density landscape. We show that this randomized bandwidth policy acts as an implicit regularization mechanism, and provide convergence theoretical results. Comparative experiments on synthetic Gaussian mixtures reveal that DSMS significantly outperforms standard and stochastic Mean-Shift baselines, exhibiting remarkable stability and preventing over-segmentation in sparse clustering scenarios witho
arXiv:2602.15393v1 Announce Type: new Abstract: Standard Mean-Shift algorithms are notoriously sensitive to the bandwidth hyperparameter, particularly in data-scarce regimes where fixed-scale density estimation leads to fragmentation and spurious modes. In this paper, we propose Doubly Stochastic Mean-Shift (DSMS), a novel extension that introduces randomness not only in the trajectory updates but also in the kernel bandwidth itself. By drawing both the data samples and the radius from a continuous uniform distribution at each iteration, DSMS effectively performs a better exploration of the density landscape. We show that this randomized bandwidth policy acts as an implicit regularization mechanism, and provide convergence theoretical results. Comparative experiments on synthetic Gaussian mixtures reveal that DSMS significantly outperforms standard and stochastic Mean-Shift baselines, exhibiting remarkable stability and preventing over-segmentation in sparse clustering scenarios without other performance degradation.
Executive Summary
This article presents Doubly Stochastic Mean-Shift (DSMS), a novel extension of the standard Mean-Shift clustering algorithm. DSMS introduces randomness in both the trajectory updates and the kernel bandwidth, allowing for a more effective exploration of the density landscape. The authors demonstrate that this randomized bandwidth policy acts as an implicit regularization mechanism, providing theoretical convergence results. Comparative experiments on synthetic Gaussian mixtures reveal that DSMS significantly outperforms standard and stochastic Mean-Shift baselines, exhibiting stability and preventing over-segmentation in sparse clustering scenarios. While DSMS shows promising results, its applicability to real-world datasets and scalability remains to be seen.
Key Points
- ▸ DSMS introduces randomness in both data samples and kernel bandwidth for improved density landscape exploration
- ▸ Randomized bandwidth policy acts as an implicit regularization mechanism
- ▸ DSMS demonstrates theoretical convergence results and outperforms baselines in comparative experiments
Merits
Strength in addressing data-scarce regimes
DSMS effectively prevents fragmentation and spurious modes in data-scarce regimes, showcasing its adaptability to challenging clustering scenarios.
Improved stability and prevention of over-segmentation
DSMS exhibits remarkable stability and prevents over-segmentation in sparse clustering scenarios without compromising performance, making it a valuable addition to existing clustering algorithms.
Demerits
Scalability limitations
The impact of DSMS on real-world datasets with large numbers of instances is unclear, and further research is necessary to ensure its scalability and applicability.
Dependence on synthetic datasets for evaluation
The authors primarily utilize synthetic Gaussian mixtures for comparative experiments, which may not accurately represent the complexities of real-world datasets.
Expert Commentary
The introduction of DSMS marks a significant advancement in the development of clustering algorithms. By effectively addressing the limitations of standard Mean-Shift algorithms, DSMS demonstrates its potential as a robust and adaptable solution for challenging clustering scenarios. However, further research is necessary to fully understand the implications of DSMS on real-world datasets and its scalability. The authors' decision to utilize synthetic datasets for evaluation is understandable, given the complexities of real-world data. Nevertheless, it is essential to consider the broader applicability of DSMS and explore its potential in various data analysis contexts.
Recommendations
- ✓ Develop and test DSMS on real-world datasets with varying sizes and complexities to evaluate its scalability and adaptability.
- ✓ Investigate the potential applications of DSMS in high-dimensional data analysis and pattern recognition tasks, where adaptability and robustness are crucial.
