Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data
Journal Publication ResearchOnline@JCUAbstract
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.
Journal
Analytica Chimica Acta
Publication Name
N/A
Volume
329
ISBN/ISSN
1873-4324
Edition
N/A
Issue
3
Pages Count
9
Location
N/A
Publisher
Elsevier
Publisher Url
N/A
Publisher Location
N/A
Publish Date
N/A
Url
N/A
Date
N/A
EISSN
N/A
DOI
10.1016/0003-2670(96)00142-0