Ecole d'ingénieur et centre de recherche en Sciences du numérique

Machine learning for nonparametric unsupervised fraud detection

Domingues, Rémi

Research report

The Dirichlet Process Mixture Model algorithm presented here aggregates the variational inference method presented by Bishop in [1], the use of a Beta prior on the Dirichlet process responsible for the mixing proportions in [2] and the use of a Gamma prior on the concentration parameter of the Dirichlet process proposed by [3]. The current variational inference algorithm approximates the posterior distribution of the dataset by a mixture of multivariate Gaussians, inferring the mixing proportions from a stick-breaking process which concentration is inferred from a Gamma distribution.

Document Bibtex

Titre:Machine learning for nonparametric unsupervised fraud detection
Département:Data Science
Eurecom ref:5694
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Research report and is available at :
Bibtex: @techreport{EURECOM+5694, year = {2018}, title = {{M}achine learning for nonparametric unsupervised fraud detection}, author = {{D}omingues, {R}{\'e}mi}, number = {EURECOM+5694}, month = {09}, institution = {Eurecom}, url = {},, }
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