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UID:20260907T061149EDT-2381IWPLle@132.216.98.100
DTSTAMP:20260907T101149Z
DESCRIPTION:Title: Statistical Inference for Multi-View Clustering\n\nAbstr
 act: In the multi-view data setting\, multiple data sets are collected on 
 a single\, common set of observations. For example\, we might perform geno
 mic and proteomic assays on a single set of tumour samples\, or we might c
 ollect relationship data from two online social networks for a single set 
 of users. It is tempting to cluster the observations using all of the data
  views\, in order to fully exploit the available information. However\, cl
 ustering the observations using all of the data views implicitly assumes t
 hat a single underlying clustering of the observations is shared across al
 l data views. If this assumption does not hold\, then clustering the obser
 vations using all data views may lead to spurious results. We seek to eval
 uate the assumption that there is some underlying relationship among the c
 lusterings from the different data views\, by asking the question: are the
  clusters within each data view dependent or independent? We develop new t
 ests for answering this question based on multivariate and/or network data
  views\, and apply them to multi-omics data from the Pioneer 100 Wellness 
 Study (Price and others\, 2017) and protein-protein interaction data from 
 the HINT database (Das and Yu\, 2012). We will also briefly discuss our cu
 rrent work on testing for no difference between the means of two estimated
  clusters in a single-view data set. This is joint work with Jacob Bien (U
 niversity of Southern California) and Daniela Witten (University of Washin
 gton).\n\n \n
DTSTART:20191209T203000Z
DTEND:20191209T213000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Lucy Gao (University of Washington)
URL:https://www.mcgill.ca/mathstat/channels/event/lucy-gao-university-washi
 ngton-303097
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