Back to Departmental Colloquium: Fall 2025
Departmental Colloquium
Date: Thursday, Nov 13, 2025
Time: 4:00PM - 5:00PM
Location: JWB 335
Kevin Miller
Brigham Young University
Title |
Adaptive algorithms for data summarization |
Abstract |
Clustering, low-rank approximation, and nonnegative matrix factorization can be phrased as data summarization problems based on selecting a small number of prototypes from a large data set. We provide a unified formulation of these problems and many others. Natural algorithms for solving these problems include adaptive search, which is a greedy deterministic selection rule, and adaptive sampling, which is a randomized selection rule based on distances to existing prototypes. We evaluate the utility of the two approaches theoretically and empirically, pointing out when each approach is preferable. |
Applied Mathematics
Data Science and Machine Learning