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Robust location and spread measures for nonparametric probability density function estimation
1Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35. 29071 Málaga, Spain. ezeqlr@lcc.uma.es
Abstract:
Robustness against outliers is a desirable property of any unsupervised learning scheme. In particular, probability density estimators benefit from incorporating this feature. A possible strategy to achieve this goal is to substitute the sample mean and the sample covariance matrix by more robust location and spread estimators. Here we use the L1-median to develop a nonparametric probability density function (PDF) estimator. We prove its most relevant properties, and we show its performance in density estimation and classification applications.
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