Ball Impurity: Measuring Heterogeneity in General
Prof. Ting LI
Associate Professor
Department of Statistics and Data Science
Southern University of Science and Technology
Date: 8 April 2026 (Wednesday)
Time: 10:30-12:00
Venue: E22-G015
Host: Prof. Yi DING Assistant Professor of Business Economics
Abstract
Data in various domains, such as neuroimaging and network data analysis, often come in complex forms without possessing a Hilbert structure. The complexity necessitates innovative approaches for effective analysis. We propose a novel measure of heterogeneity, ball impurity, which is designed to work with complex non-Euclidean objects. Our approach extends the notion of impurity to general metric spaces, providing a versatile tool for feature selection and tree models. The ball impurity measure exhibits desirable properties, such as the triangular inequality, and is computationally tractable, enhancing its practicality and usefulness. Extensive experiments on synthetic data and real data from the UK Biobank validate the efficacy of our approach in capturing data heterogeneity. Remarkably, our results compare favorably with state-of-the-art methods in metric spaces, highlighting the potential of ball impurity as a valuable tool for addressing complex data analysis tasks.
Speaker
Prof. Ting LI is a Research Fellow and Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology. He has led one NSFC Youth Project and received a National High-Level Talent Program (Youth) grant. His research focuses on statistical analysis of complex data and large-scale and generative models, primarily including network structure data research, gene correlation analysis of complex phenotypes, and human brain image data analysis. He has published 20 papers in core statistics and biostatistics journals and top machine learning conferences, including Annals of Statistics, JASA, JMLR, Annals of Applied Statistics, Genome Research, Human Brain Mapping, and ICML.
All are welcome!
