2016/10/13 by Yunhua Xiang, Xiang, Yunhua, Yen‐Chi Chen +2 · 1 citation
Computer Science · Mathematics · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Learning (stat.ML) #Methodology (stat.ME) #Time Series Analysis and Forecasting #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.1610.03927
19 page, 5 figures, 3 tables
arxiv created 2016/10/13 · openalex publication_date 2016/10/13 · arxiv updated 2016/10/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we study how the mean shift algorithm can be used to denoise a dataset. We introduce a new framework to analyze the mean shift algorithm as a denoising approach by viewing the algorithm as an operator on a distribution function. We investigate how the mean shift algorithm changes the distribution and show that data points shifted by the mean shift concentrate around high density regions of the underlying density function. By using the mean shift as a denoising method, we enhance the performance of several clustering techniques, improve the power of two-sample tests, and obtain a new method for anomaly detection.