Class PWMEstimator

  • All Implemented Interfaces:
    DistanceBasedIntrinsicDimensionalityEstimator, IntrinsicDimensionalityEstimator<java.lang.Object>

    @Reference(authors="L. Amsaleg, O. Chelly, T. Furon, S. Girard, M. E. Houle, K. Kawarabayashi, M. Nett",title="Estimating Local Intrinsic Dimensionality",booktitle="Proc. SIGKDD International Conference on Knowledge Discovery and Data Mining 2015",url="https://doi.org/10.1145/2783258.2783405",bibkey="DBLP:conf/kdd/AmsalegCFGHKN15") @Reference(authors="J. Maciunas Landwehr, N. C. Matalas, J. R. Wallis",title="Probability weighted moments compared with some traditional techniques in estimating Gumbel parameters and quantiles",booktitle="Water Resources Research 15(5)",url="https://doi.org/10.1029/WR015i005p01055",bibkey="doi:10.1029/WR015i005p01055")
    public class PWMEstimator
    extends java.lang.Object
    implements DistanceBasedIntrinsicDimensionalityEstimator
    Probability weighted moments based estimator.

    Reference:

    L. Amsaleg, O. Chelly, T. Furon, S. Girard, M. E. Houle, K. Kawarabayashi, M. Nett
    Estimating Local Intrinsic Dimensionality
    Proc. SIGKDD Int. Conf. on Knowledge Discovery and Data Mining

    We use the unbiased weights of Maciunas Landwehr et al.:

    J. Maciunas Landwehr, N. C. Matalas, J. R. Wallis
    Probability weighted moments compared with some traditional techniques in estimating Gumbel parameters and quantiles
    Water Resources Research 15(5)

    but we pretend we had one additional data point at 0, to not lose valuable data. When implemented exactly, we would have to assign a weight of 0 to the first point. But since we are not using the mean, we don't want to do this. This hack causes this estimator to have a bias to underestimate the ID.

    Since:
    0.7.0
    Author:
    Jonathan von Br√ľnken, Erich Schubert