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SUMMARY:Ben Draves (BU)
DTSTART:20201022T140000Z
DTEND:20201022T143000Z
DTSTAMP:20260423T004514Z
UID:BUcomm/2
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/BUcomm/2/">C
 ommon Principal Component Analysis</a>\nby Ben Draves (BU) as part of BU C
 ommunity Seminar\n\n\nAbstract\nDimensionality reduction attempts to trans
 form often high dimensional data into a lower dimensional representation w
 hile maintaining the data's intrinsic properties. Several methods have bee
 n developed to accomplish this task\, but perhaps the most widely used is 
 Principal Component Analysis (PCA). While PCA is well known\, its extensio
 n to multiple populations\, Common Principle Component Analysis (CPCA)\, i
 s much lesser known. In this talk we introduce CPCA and discuss its effica
 cy for completing dimensionality reduction across multiple populations. In
  addition\, we discuss spectral approaches for fitting CPCA in practice\, 
 including randomized algorithms for truncated singular value decomposition
 s. Finally\, we employ CPCA for simultaneous dimensionality reduction acro
 ss penguin species in the Palmer Penguin dataset.\n
LOCATION:https://researchseminars.org/talk/BUcomm/2/
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