BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Sparse and Low-Rank Subspace Clustering
DTSTART:20130425T140000
DTSTAMP:20260407T105432Z
UID:415a8f2788776fa16552d1e286ede465ba090aefac3c150927ed919b
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Rene Vidal\, Johns Hopkins University\nBio: Professor Vi
 dal received his B.S. degree in Electrical Engineering (highest honors) fr
 om the Pontificia Universidad Catolica de Chile in 1997 and his M.S. and P
 h.D. degrees in Electrical Engineering and Computer Sciences from the Univ
 ersity of California at Berkeley in 2000 and 2003\, respectively. He is cu
 rrently an Associate Professor in the Center for Imaging Science in the De
 partment of Biomedical Engineering of The Johns Hopkins University. His re
 search interest are biomedical image analysis\, computer vision\, machine 
 learning\, hybrid systems\, robotics and signal processing. Dr. Vidal has 
 received numerous awards for his work\, including the IAPR 2012 J.K. Aggar
 wal Prize for ``outstanding contributions to generalized principal compone
 nt analysis (GPCA) and subspace clustering in computer vision and pattern 
 recognition"\, the 2012 Best Paper Award in Medical Robotics and Computer 
 Assisted Interventions\, the 2012 Best Paper Award at the Conference on De
 cision and Control\, the 2009 ONR Young Investigator Award\, the 2009 Sloa
 n Research Fellowship\, the 2005 NFS CAREER Award and the 2004 Best Paper 
 Award Honorable Mention at the European Conference on Computer Vision. He 
 is Associate Editor of the IEEE Transactions on Pattern Analysis and Machi
 ne Intelligence\, the SIAM Journal on Imaging Sciences and the Journal of 
 Mathematical Imaging and Vision\, and has served as an area chair or progr
 am committee member for all major conferences in computer vision and medic
 al imaging. He is a senior member of the IEEE and a member of the ACM.\nIn
  the era of data deluge\, the development of methods for discovering struc
 ture in high-dimensional data is becoming increasingly important. Traditio
 nal approaches often assume that the data is sampled from a single low-dim
 ensional manifold. However\, in many applications in signal/image processi
 ng\, machine learning and computer vision\, data in multiple classes lie i
 n multiple low-dimensional subspaces of a high-dimensional ambient space. 
 In this talk\, I will present methods from algebraic geometry\, sparse rep
 resentation theory and rank minimization for clustering and classification
  of data in multiple low-dimensional subspaces. I will show how these meth
 ods can be extended to handle noise\, outliers as well as missing data. I 
 will also present applications of these methods to video segmentation and 
 face clustering.
LOCATION:CE 1 5 https://plan.epfl.ch/?room==CE%201%205
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR