Large Matrices beyond Singular Value Decomposition on March 22, 2010

DMSIG -- Large Matrices beyond Singular Value Decomposition on March 22, 2010

Slides are posted here:
http://www.sfbayacm.org/?p=1326


Date: Monday March 22, 2010; 6:30 pm

Cost: Free and open to all who wish to attend, but membership is only $20/year. Anyone may join our mailing list at no charge, and receive announcements of upcoming events.

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

SPEAKER BIOGRAPHY

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 .

Category:

Science & Technology

Tags:

• acm csedweek education
• andrea montanari
• data mining
• data mining sig
• Machine Learning
• singular value decomposition
• stanford