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SUMMARY:Sparse and Low-Rank Subspace Clustering
DTSTART:20130425T140000
DTEND:20130425T150000
DTSTAMP:20260407T144213Z
UID:a24e844931194a9f9a88d9363fbf77a7c9b80147f83a85fe55eb1ade
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Rene Vidal\, Center for Imaging Science\, Department of 
 Biomedical Engineering\, The Johns Hopkins UniversityBiography: Professor 
 Vidal received his B.S. degree in Electrical Engineering (highest honors) 
 from the Pontificia Universidad Catolica de Chile in 1997 and his M.S. and
  Ph.D. degrees in Electrical Engineering and Computer Sciences from the Un
 iversity of California at Berkeley in 2000 and 2003\, respectively. He is 
 currently an Associate Professor in the Center for Imaging Science in the 
 Department of Biomedical Engineering of The Johns Hopkins University. His 
 research interest are biomedical image analysis\, computer vision\, machin
 e learning\, hybrid systems\, robotics and signal processing. Dr. Vidal ha
 s received numerous awards for his work\, including the IAPR 2012 J.K. Agg
 arwal Prize for ``outstanding contributions to generalized principal compo
 nent analysis (GPCA) and subspace clustering in computer vision and patter
 n recognition"\, the 2012 Best Paper Award in Medical Robotics and Compute
 r Assisted Interventions\, the 2012 Best Paper Award at the Conference on 
 Decision and Control\, the 2009 ONR Young Investigator Award\, the 2009 Sl
 oan Research Fellowship\, the 2005 NFS CAREER Award and the 2004 Best Pape
 r Award Honorable Mention at the European Conference on Computer Vision. H
 e is Associate Editor of the IEEE Transactions on Pattern Analysis and Mac
 hine Intelligence\, the SIAM Journal on Imaging Sciences and the Journal o
 f Mathematical Imaging and Vision\, and has served as an area chair or pro
 gram committee member for all major conferences in computer vision and med
 ical imaging. He is a senior member of the IEEE and a member of the ACM.\n
 Abstract: In the era of data deluge\, the development of methods for disco
 vering structure in high-dimensional data is becoming increasingly importa
 nt. Traditional approaches often assume that the data is sampled from a si
 ngle low-dimensional manifold. However\, in many applications in signal/im
 age processing\, machine learning and computer vision\, data in multiple c
 lasses lie in multiple low-dimensional subspaces of a high-dimensional amb
 ient space. In this talk\, I will present methods from algebraic geometry\
 , sparse representation theory and rank minimization for clustering and cl
 assification of data in multiple low-dimensional subspaces. I will show ho
 w these methods can be extended to handle noise\, outliers as well as miss
 ing data. I will also present applications of these methods to video segme
 ntation and face clustering.
LOCATION:CE 1 5 https://plan.epfl.ch/?room==CE%201%205
STATUS:CONFIRMED
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