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Projection Pursuit methodology permits to solve the difficult problem offinding an estimate of a density defined on a set of very large dimension. Inhis seminal article, Huber (see "Projection pursuit", Annals of Statistics,1985) evidences the interest of the Projection Pursuit method thanks to thefactorisation of a density into a Gaussian component and some residual densityin a context of Kullback-Leibler divergence maximisation. In the presentarticle, we introduce a new algorithm, and in particular a test for thefactorisation of a density estimated from an iid sample.

Projection Pursuit through Relative Entropy Minimization