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Please use this identifier to cite or link to this item: http://hdl.handle.net/10197/3455

Title: Semi-supervised linear discriminant analysis
Authors: Toher, Deirdre
Downey, Gerard
Murphy, Thomas Brendan
Keywords: Classification
Discriminant analysis
Food authenticity
Chemometrics--Classification
Food--Analysis
Issue Date: 2-Jul-2012
Publisher: Wiley
Citation: Toher, Deirdre, Downey, Gerard, Murphy, Thomas Brendan : Semi-supervised linear discriminant analysis. Journal of Chemometrics, 25 (12) 2011-12, pp.624-630
Abstract: Fisher's linear discriminant analysis is one of the most commonly used and studied classification methods in chemometrics. The method finds a projection of multivariate data into a lower dimensional space so that the groups in the data are well separated. The resulting projected values are subsequently used to classify unlabeled observations into the groups. A semi-supervised version of Fisher's linear discriminant analysis is developed, so that the unlabeled observations are also used in the model fitting procedure. This approach is advantageous when few labeled and many unlabeled observations are available. The semi-supervised linear discriminant analysis method is demonstrated on a number of data sets where it is shown to yield better separation of the groups and improved classification over Fisher's linear discriminant analysis.
Description: peer-reviewed
URI: http://hdl.handle.net/10197/3455
http://dx.doi.org/10.1002/cem.1408
ISSN: 1099-128X
Other Identifiers: http://hdl.handle.net/10197/3455
Appears in Collections:Teagasc funded research
Food Chemistry & Technology

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