Robust Location and Spread Measures for Nonparametric Probability Density Function Estimation

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… (More)
DOI: 10.1142/S0129065709002075

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Cite this paper

@article{LpezRubio2009RobustLA, title={Robust Location and Spread Measures for Nonparametric Probability Density Function Estimation}, author={Ezequiel L{\'o}pez-Rubio}, journal={International journal of neural systems}, year={2009}, volume={19 5}, pages={345-57} }