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Efficient Parameterization for the Dimensional Reduction Problem
Linköping University, Department of Physics, Chemistry and Biology. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2003 (English)Report (Other academic)
Abstract [en]

A new method to optimize with orthonormal constraints is described, where a particular composition of plane (Givens) rotations is used to parameterize decision variables in terms of angles. It is showed that this parameterization is complete and that any orthonormal k-by-nmatrix can be derived to a set of no more than kn-k(k+1) angles. The technique is applied to the emph {feature extraction problem} where a linear subspace is optimized with respect to non-linear objective functions. The Optimal Discriminative Projection (ODP) algorithm is described. ODP is a data compression or feature extraction algorithm that combines powerful model optimization with regularization to avoid over training. The ODP is used primarily for classification problems.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2003. , 18 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2535
Keyword [en]
Optimization, Data compression, Pattern recognition
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-55954ISRN: LiTH-ISY-R-2535OAI: diva2:316785
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-02Bibliographically approved

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Spångéus, PerLindgren, David
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