Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data

Wu, W., Mallet, Y., Walczak, B., Penninckx, W., Massart, D.L., Heuerding, S., and Erni, F. (1996) Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data. Analytica Chimica Acta, 329 (3). pp. 257-265.

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Abstract

Three classifiers, namely linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and regularized discriminant analysis (RDA) are considered in this study for classification based on NIR data. Because NIR data sets are severely ill-conditioned, the three methods cannot be directly applied. A feature selection method was used to reduce the data dimensionality, and the selected features were used as the input of the classifiers. RDA can be considered as an intermediate method between LDA and QDA, and in several cases, RDA reduces to either LDA or QDA depending on which is better. In some other cases, RDA is somewhat better. However, optimization is time consuming. It is therefore concluded that in many cases, LDA or QDA should be recommended for practical use, depending on the characteristics of the data. However, in those cases where even small gains in classification quality are important, the application of RDA might be useful.

Item ID: 39727
Item Type: Article (Refereed Research - C1)
Keywords: regularized discriminant analysis; linear discriminant, analysis; quadratic discriminant analysis; near infrared spectroscopy; feature selection; pattern recognition
ISSN: 1873-4324
Date Deposited: 02 Sep 2015 03:32
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970103 Expanding Knowledge in the Chemical Sciences @ 100%
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