Sparse Adaptive Dirichlet-Multinomial-like Processes

Marcus Hutter (ANU)

NICTA SML SEMINAR

DATE: 2013-07-11
TIME: 11:15:00 - 12:15:00
LOCATION: NICTA - 7 London Circuit
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ABSTRACT:
Online estimation and modelling of i.i.d. data for short sequences over large or complex ``alphabets'' is a ubiquitous (sub)problem in machine learning, information theory, data compression, statistical language processing, and document analysis. The Dirichlet-Multinomial distribution (also called Polya urn scheme) and extensions thereof are widely applied for online i.i.d. estimation. Good a-priori choices for the parameters in this regime are difficult to obtain though. I derive an optimal adaptive choice for the main parameter via tight, data-dependent redundancy bounds for a related model. The 1-line recommendation is to set the 'total mass' = 'precision' = 'concentration' parameter to m/2ln(n+1)/m, where n is the (past) sample size and m the number of different symbols observed (so far). The resulting estimator is simple, online, fast, and experimental performance is superb.
BIO:
http://www.hutter1.net/official/vitae.pdf