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PRODID:-//University of Utah Math Department//Adaptive algorithms for data summarization//EN
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X-WR-CALNAME:Adaptive algorithms for data summarization
X-WR-CALDESC:Adaptive algorithms for data summarization at University of Utah Mathematics Department
X-WR-TIMEZONE:America/Denver
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UID:20251113T160000-kevin-miller@math.utah.edu
DTSTART;TZID=America/Denver:20251113T160000
DTEND;TZID=America/Denver:20251113T170000
DTSTAMP:20260922T150858Z
SUMMARY:Adaptive algorithms for data summarization
DESCRIPTION:Speaker: Kevin Miller\, Brigham Young University\n\nClustering, 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 …

LOCATION:JWB 335

URL:https://www.math.utah.edu/research/colloquia/2025-11-13-kevin-miller/
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