Variable selection for discriminant analysis of fish sounds using matrix correlations

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.

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