Toward a generic representation of random variables for machine learning

Gautier Marti (), Philippe Very and Philippe Donnat ()
Additional contact information
Gautier Marti: X - École polytechnique - IP Paris - Institut Polytechnique de Paris, Hellebore Capital Management
Philippe Very: Hellebore Capital Management
Philippe Donnat: Hellebore Capital Management

Working Papers from HAL

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.

Date: 2015-09-14
New Economics Papers: this item is included in nep-ecm and nep-mac
Note: View the original document on HAL open archive server: https://hal.science/hal-01196883
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://hal.science/hal-01196883/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-01196883

Access Statistics for this paper

More papers in Working Papers from HAL
Bibliographic data for series maintained by CCSD ().