组织者 / Organizer
吴宇楠
报告人 / Speaker
叶成龙 助理教授
肯塔基大学
时间 / Time
14:00-15:00, Friday
Dec. 26, 2025
地点 / Venue
C548
Shuangqing Complex Building A
Abstract
Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensional embeddings before partitioning, which embarks unique evaluation challenges. Traditional clustering validation measures, designed for low-dimensional spaces, are problematic for deep clustering for two reasons: 1) the curse of dimensionality when applied to the high-dimensional input data, and 2) unreliable comparison of clustering results when applied to embedded data from different embedding spaces, owing to variations in training procedures and model parameter settings. This paper addresses these unresolved and often overlooked challenges in evaluating clustering within deep learning. We propose a systematic evaluation framework for internal clustering validation measures that: (1) theoretically establishes why traditional measures are ineffective when applied to input data or across disparate embedding spaces paired with partitioning outcomes; (2) identifies embedding spaces that endorse reliable evaluations by detecting groups with high agreement in ranking partitioning outcomes; and (3) develops a stable and robust scoring scheme by weighting index values computed across these identified embedding spaces. Experiments show that this new framework aligns better with external measures, effectively reducing the misguidance from the improper use of internal validation measures in deep clustering evaluation.
About the Speaker
Chenglong Ye is currently an assistant professor in the Dr. Bing Zhang Department of Statistics, University of Kentucky.
He received his Ph.D. in Statistics from University of Minnesota in June, 2019 under the supervision of Professor Yuhong Yang. Before his PhD studies, he received his B.S. in Statistics from University of Science and Technology of China (USTC) in 2014.
