[Ml-stat-talks] Fwd: S.S. Wilks Memorial lecture: Downsize Those Eigenvalues! by David L. Donoho

Barbara Engelhardt bee at princeton.edu
Mon Apr 10 15:02:12 EDT 2017


Wilks Memorial Lecture next Monday.



**** S.S. Wilks Memorial Lecture *** *You are cordially invited to the *S.S.
Wilks Memorial Lecture* on *Monday, April 17* at *4:45p*m in *Computer
Science 104*.

*Downsize Those Eigenvalues! *
By  David L. Donoho, Stanford University

*ABSTRACT**: * Principal Components Analysis and Factor Analysis are used
heavily across science and technology, in fields from population genetics
to empirical finance. In recent years these methods were heavily used in
'Big Data' settings with very large datasets, often with as many
variables/observables as observations/individuals/patients. 'Big Data'
breaks the original justification for such methods. Tens of thousands of
users are bound to be disappointed and frustrated with the results they
actually get.

Thanks to recent advances in Random Matrix Theory, we can understand the
disappointments that people are likely to face -- and propose corrected
approaches. An important correction is to cut eigenvalues down to size,
sometimes radically.

*BIO:  *David L. Donoho is a Professor of Statistics and Anne T and Robert
M Bass Professor in the Humanities and Sciences at Stanford University. He
earned his AB in Statistics from Princeton and his PhD in Statistics from
Harvard. He was co-founder of network management software company BigFix
and has worked for Western Geophysical Company and Renaissance
Technologies. He has published research in robust statistics, mathematical
statistics, signal and image processing, harmonic analysis, scientific
computing, and high dimensional geometry. He is a member of the US National
Academy of Sciences as well as a foreign associate of the French Académie
des Sciences. His work has been recognized by a MacArthur Fellow, the COPSS
Presidents Award, John von Neumann Prize, Norbert Wiener Prize, Shaw Prize,
among others.
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