Semi-supervised linear discriminant analysis
Toher, Deirdre Toher, Deirdre Downey, Gerard Downey, Gerard Murphy, Thomas Brendan Murphy, Thomas Brendan
Toher, Deirdre
Toher, Deirdre
Downey, Gerard
Downey, Gerard
Murphy, Thomas Brendan
Murphy, Thomas Brendan
Citations
Altmetric:
Date
02/07/2012
Research Projects
Organizational Units
Journal Issue
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.