Quantifying multivariate classification performance: the problem of overfitting

Brian R. Stallard; John G. Taylor

doi:10.1117/12.366304

27 October 1999 Quantifying multivariate classification performance: the problem of overfitting

Brian R. Stallard, John G. Taylor

Proceedings Volume 3753, Imaging Spectrometry V; (1999) https://doi.org/10.1117/12.366304
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

We have been studying the use of spectral imagery to locate targets in spectrally interfering backgrounds. In making performance estimates for various sensors it has become evident that some calculations are unreliable because of overfitting. Hence, we began a thorough study of the problem of overfitting in multivariate classification. In this paper we present some model based results describing the problem. From the model we know the ideal covariance matrix, the ideal discriminant vector, and the ideal classification performance. We then investigate how experimental conditions such as noise, number of bands, and number of samples cause discrepancies from the ideal results. We also suggest ways to discover and alleviate overfitting.

Citation Download Citation

Brian R. Stallard and John G. Taylor "Quantifying multivariate classification performance: the problem of overfitting", Proc. SPIE 3753, Imaging Spectrometry V, (27 October 1999); https://doi.org/10.1117/12.366304

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