Large Matricies beyond Singular Value Decomposition 3/22/2010 March 22, 2010

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Date: Monday March 22, 2010; 6:30 pm Notice: NEW MEETING day of Month for 2010 – the fourth Monday of each month!
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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.
Machine Learning, Data Mining, Singular Value Decomposition, dimensionality reduction, large data sets, Netflix contest, Andrea Montanari, Stanford, Statistics, Collaborative Filtering

Official Website: http://www.sfbayacm.org/?cat=5