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Data Intelligence, Business Analytics
DMSIG – Large Matricies beyond Singular Value Decomposition 3/22/2010
Location: LinkedIn, 2027 Stierlin Ct., Mountain View, CA 94043.
Date: Monday March 22, 2010; 6:30 pm
Cost: Free
Speakers: Andrea Montanari, Stanford Professor in Electrical Engineering and Statistics
Title: “Large Matrices beyond Singular Value Decomposition”
A number of data sets are naturally described in matrix form. Examples range from micro-arrays to collaborative filtering data. In many
of these examples, singular value decomposition (SVD) provides an efficient way
to construct a low-rank approximation thus achieving a large dimensionality
reduction. SVD is also an important tool in the design of approximate
linear algebra algorithms for massive data sets. It is a recent discovery
that –for ‘generic’ matrices — SVD is sub-optimal, and can be significantly
improved upon. There has been considerable progress on this topic over
the last year, partly spurred by interest in the Netflix challenge. I
will overview this progress.
Andrea Montanari received a Laurea degree in Physics
in 1997, and a Ph. D. in Theoretical Physics in 2001 (both from Scuola Normale
Superiore in Pisa, Italy). He has been post-doctoral fellow at Laboratoire de
Physique Théorique de l’Ecole Normale Supérieure (LPTENS), Paris, France, and the Mathematical Sciences Research Institute, Berkeley, USA. Since 2002 he is Chargé de Recherche (a permanent
research position with Centre National de la Recherche Scientifique, CNRS) at
LPTENS.
In September 2006 he joined Stanford University as Assistant Professor in the Departments of
Electrical Engineering and Statistics.
He was co-awarded the ACM SIGMETRICS best paper award in 2008. He received the CNRS bronze medal for theoretical physics in 2006 and the National Science Foundation CAREER award in 2008.
Tags: Andrea, Collaborative, Data, Decomposition, Filtering, Learning, Machine, Mining, Montanari, Netflix, More…
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