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Toward a generic representation of random variables for machine learning

Author

Listed:
  • Gautier Marti

    (X - École polytechnique - IP Paris - Institut Polytechnique de Paris, Hellebore Capital Management)

  • Philippe Very

    (Hellebore Capital Management)

  • Philippe Donnat

    (Hellebore Capital Management)

Abstract

This paper presents a pre-processing and a distance which improve the performance of machine learning algorithms working on independent and identically distributed stochastic processes. We introduce a novel non-parametric approach to represent random variables which splits apart dependency and distribution without losing any information. We also propound an associated metric leveraging this representation and its statistical estimate. Besides experiments on synthetic datasets, the benefits of our contribution is illustrated through the example of clustering financial time series, for instance prices from the credit default swaps market. Results are available on the website www.datagrapple.com and an IPython Notebook tutorial is available at www.datagrapple.com/Tech for reproducible research.

Suggested Citation

  • Gautier Marti & Philippe Very & Philippe Donnat, 2015. "Toward a generic representation of random variables for machine learning," Working Papers hal-01196883, HAL.
    • Handle: RePEc:hal:wpaper:hal-01196883
    Note: View the original document on HAL open archive server: https://hal.science/hal-01196883
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    References listed on IDEAS

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