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Semi‐supervised linear discriminant analysis
Author(s) -
Toher Deirdre,
Downey Gerard,
Murphy Thomas Brendan
Publication year - 2011
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1408
Subject(s) - linear discriminant analysis , optimal discriminant analysis , chemometrics , mathematics , kernel fisher discriminant analysis , pattern recognition (psychology) , projection (relational algebra) , artificial intelligence , discriminant , multiple discriminant analysis , principal component analysis , multivariate statistics , statistics , computer science , machine learning , algorithm , facial recognition system
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.