Modern Nonparametric Statistics on Modern Big Data

author: Alexander Gray, School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology
published: Jan. 16, 2013,   recorded: December 2012,   views: 6322

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Modern data is increasing very large in terms of both the number of objects and the number of dimensions. While in a statistical sense massive amounts of data make nonparametric methods entirely appropriate, their computational cost has made practitioners typically conclude that they are not possible in such scenarios. I will review a few common conceptual classes of nonparametric methods, including both classical and modern variants, then review recent algorithmic advances which can make nonparametric methods tractable

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