基于相对熵最小化的投影寻踪
Projection Pursuit through Relative Entropy Minimization
摘要：
投影寻踪方法允许解决困难的问题，找到一个估计的密度定义在一组非常大的尺寸。在他的开创性文章中，Huber（参见“Projection pursuit”，Annals of Statistics，1985）证明了投影寻踪法的重要性，这要归功于在Kullback-Leibler发散最大化的背景下将密度分解为高斯分量和一些剩余密度。在本文中，我们介绍了一种新的算法，特别是一个测试的因式分解的密度估计从iid样本。
英文摘要：
Projection Pursuit methodology permits to solve the difficult problem of finding an estimate of a density defined on a set of very large dimension. In his seminal article, Huber (see "Projection pursuit", Annals of Statistics, 1985) evidences the interest of the Projection Pursuit method thanks to the factorisation of a density into a Gaussian component and some residual density in a context of Kullback-Leibler divergence maximisation. In the present article, we introduce a new algorithm, and in particular a test for the factorisation of a density estimated from an iid sample.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
