@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Sanjit Seshia's publication pages at http://www.eecs.berkeley.edu/~sseshia
@InProceedings{sadigh-ifac14,
  author = 	 {Dorsa Sadigh and Henrik Ohlsson and S. Shankar Sastry and Sanjit A. Seshia},
  title = 	 {Robust Subspace System Identification via Weighted Nuclear Norm Optimization},
  booktitle = 	 {Proceedings of the 19th World Congress of the International Federation of Automatic Control (IFAC)},
  OPTcrossref =  {},
  OPTkey = 	 {},
  OPTpages = 	 {},
  year = 	 {2014},
  OPTeditor = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  OPTaddress = 	 {},
  month = 	 {August},
  OPTorganization = {},
  OPTpublisher = {},
  OPTannote = 	 {},
  abstract = {Subspace identification is a classical and very well studied problem in system 
identification. The problem was recently posed as a convex optimization problem via the nuclear 
norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The 
proposed framework takes the form of a convex optimization problem with an objective that 
trades off fit, rank and sparsity. As in robust PCA, it can be problematic to find a suitable 
regularization parameter.We show how the space in which a suitable parameter should be sought 
can be limited to a bounded open set of the two-dimensional parameter space. In practice, this 
is very useful since it restricts the parameter space that is needed to be surveyed.}, 
}