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? 16th IFAC Symposium on System Identification (Sysid 2012), Brussels, BE, 11 - 13 Jul 2012. 6pp.

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Description/Abstract

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.

Item Type: Conference or Workshop Item (Invited Paper)
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Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 336088
Date Deposited: 14 Mar 2012 17:16
Last Modified: 27 Mar 2014 20:19
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/336088

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