Semi-supervised linear discriminant analysis

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Title: Semi-supervised linear discriminant analysis
Authors: Toher, Deirdre
Downey, Gerard
Murphy, Thomas Brendan
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Date: Dec-2011
Online since: 2012-01-30T12:38:51Z
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.
Funding Details: Science Foundation Ireland
Other funder
Type of material: Journal Article
Publisher: Wiley
Journal: Journal of Chemometrics
Volume: 25
Issue: 12
Start page: 624
End page: 630
Copyright (published version): 2011 John Wiley & Sons, Ltd.
Keywords: ClassificationDiscriminant analysisFood authenticity
Subject LCSH: Discriminant analysis
DOI: 10.1002/cem.1408
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Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection

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