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Robust Subspace System Identification via Weighted Nuclear Norm Optimization
University of Calif Berkeley, CA 94720 USA.
Linköping University, Department of Electrical Engineering, Electronics System. Linköping University, The Institute of Technology. University of Calif Berkeley, CA 94720 USA.
University of Calif Berkeley, CA 94720 USA.
University of Calif Berkeley, CA 94720 USA.
2014 (English)In: IFAC PAPERSONLINE, ELSEVIER SCIENCE BV , 2014, Vol. 47, no 3, 9510-9515 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2014. Vol. 47, no 3, 9510-9515 p.
Keyword [en]
subspace identification; robust estimation; outliers; nuclear norm; sparsity; robust PCA
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-137421ISI: 000391109200085OAI: oai:DiVA.org:liu-137421DiVA: diva2:1095566
Conference
19th World Congress of the International-Federation-of-Automatic-Control (IFAC)
Available from: 2017-05-15 Created: 2017-05-15 Last updated: 2017-05-15

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