Comparison of discriminant analysis methods applied to stellar data classification

Details Comparison of discriminant analysis methods applied to stellar data classification Comparison of discriminant analysis methods applied to stellar data classification

Sep 2003

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003spie.5286..758w&link_type=abstract

Third International Symposium on Multispectral Image Processing and Pattern Recognition. Edited by Lu, Hanqing; Zhang, Tianxu.

Computer Science

Scientific paper

In this study, five classifiers, namely quadratic discriminant analysis, linear discriminant analysis, regularlized discriminant analysis, leave-one-out covariance matrix estimate and Killback-Leibler information measure based method are considered for classification of stellar spectra data. Because stellar spectra data sets are severly ill-posed, we first adopt some feature selection method such as principal component analysis to reduce data dimensionality. The input of the classifiers are those selected features, and the cross-validation technique is used to optimize the regularization parameters. Experimental results show that in most cases, regularized classifiers are high classification rates than that of quadratic discriminant analysis, but parameter optimization is time consuming. From experiments of exhaustive searching regularization parameter, it is found that in some cases cross-validation method is not always good in the selection of models.

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