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A Novel Feature Extraction Algorithm for Asymmetric Classification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Physics, Chemistry and Biology. Linköping University, The Institute of Technology.
2004 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 4, 643-650 p.Article in journal (Refereed) Published
Abstract [en]

A linear feature extraction technique for asymmetric distributions is introduced, the asymmetric class projection (ACP). By emph {asymmetric classification} is understood discrimination among distributions with different covariance matrices. Two distributions with unequal covariance matrices do not in general have a symmetry plane, a fact that makes the analysis more difficult compared to the symmetric case. The ACP is similar to linear discriminant analysis (LDA) in the respect that both aim at extracting discriminating features (linear combinations or projections) from many variables. However, the drawback of the well known LDA is the assumption of symmetric classes with separated centroids. The ACP, incontrast, works on (two) possibly concentric distributions with unequal covariance matrices. The ACP is tested on data from anarray of semiconductor gas sensors with the purpose of distinguish bad grain from good.

Place, publisher, year, edition, pages
IEEE Sensors Council, 2004. Vol. 4, 643-650 p.
Keyword [en]
Pattern Recognition, Classification, System Identification
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-22504DOI: 10.1109/JSEN.2004.833521Local ID: 1754OAI: diva2:242817
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2013-08-28

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Lindgren, David
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