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How effective is the nuclear norm heuristic in solving data approximation problems?

Markovsky, Ivan (2012) How effective is the nuclear norm heuristic in solving data approximation problems? At 16th IFAC Symposium on System Identification (Sysid 2012), Belgium. 11 - 13 Jul 2012. 6 pp.

Record type: Conference or Workshop Item (Other)


The question in the title is answered empirically by solving instances of three classical problems: fitting a straight line to data, fitting a real exponent to data, and system identification in the errors-in-variables setting. The results show that the nuclear norm heuristic performs worse than alternative problem dependant methods---ordinary and total least squares, Kung's method, and subspace identification. In the line fitting and exponential fitting problems, the globally optimal solution is known analytically, so that the suboptimality of the heuristic methods is quantified.

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Submitted date: March 2012
Published date: July 2012
Venue - Dates: 16th IFAC Symposium on System Identification (Sysid 2012), Belgium, 2012-07-11 - 2012-07-13
Organisations: Southampton Wireless Group


Local EPrints ID: 336088
PURE UUID: a63e0acd-fdce-407e-83fb-eb5083567b4f

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Date deposited: 14 Mar 2012 17:16
Last modified: 18 Jul 2017 06:09

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Author: Ivan Markovsky

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