NDLI: Variable selection for discriminant analysis of fish sounds using matrix correlations

Content Provider	Springer Nature Link
Author	Wood, Mark Jolliffe, Ian T. Horgan, Graham W.
Copyright Year	2005
Abstract	Discriminant analysis is a widely used multivariate technique. In some applications the number of variables available is very large and, as with other multivariate techniques, it is desirable to simplify matters by selecting a subset of the variables in such a way that little useful information is lost in doing so. Many methods have been suggested for variable selection in discriminant analysis; this article introduces a new one, based on matrix correlation, an idea that has proved useful in the context of principal component analysis. The method is illustrated on an example involving fish sounds. It is important to discriminate between the sounds made by different species of fish, and even by individual fish, but the nature of the data is such that many potential variables are available.
Starting Page	321
Ending Page	336
Page Count	16
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	10
Issue Number	3
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Canonical variate analysis Feature selection Genetic algorithm Generalized coefficient of determination Wavelets Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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