Semi-supervised linear discriminant analysis

• Published in: Journal of chemometrics 2011-12, Vol.25 (12), p.621-630
• Main Authors: Toher, Deirdre, Downey, Gerard, Murphy, Thomas Brendan
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Chemistry; Classification; Discriminant analysis; Exact sciences and technology; food authenticity; Food industries; Fundamental and applied biological sciences. Psychology; General and physical chemistry; General. Nomenclature, chemical documentation, computer chemistry; Multivariate analysis; Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry
• ISSN: 0886-9383; 1099-128X
• DOI: 10.1002/cem.1408

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. Copyright © 2011 John Wiley & Sons, Ltd. A semi‐supervised version of Fisher's linear discriminant analysis is developed so that the both labelled and unlabeled observations are 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.