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PRODID:-//University of Utah Math Department//Geometric and Statistical Approaches to Shallow and Deep Clustering//EN
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X-WR-CALNAME:Geometric and Statistical Approaches to Shallow and Deep Clustering
X-WR-CALDESC:Geometric and Statistical Approaches to Shallow and Deep Clustering at University of Utah Mathematics Department
X-WR-TIMEZONE:America/Denver
BEGIN:VEVENT
UID:20210930T160000-james-murphy@math.utah.edu
DTSTART;TZID=America/Denver:20210930T160000
DTEND;TZID=America/Denver:20210930T170000
DTSTAMP:20260922T150857Z
SUMMARY:Geometric and Statistical Approaches to Shallow and Deep Clustering
DESCRIPTION:Speaker: James Murphy\, Tufts University\n\nWe propose approaches to unsupervised clustering based on data-dependent distances and dictionary learning. By considering metrics derived from data-driven graphs, robustness to noise and ambient dimensionality is achieved. Connections to geometric analysis, stochastic processes, and deep learning …

LOCATION:JWB 335

URL:https://www.math.utah.edu/research/colloquia/2021-09-30-james-murphy/
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