Sparse Sequential Dirichlet Coding
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Veness, Joel
Hutter, Marcus
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Abstract
This short paper describes a simple coding technique, Sparse Sequential Dirichlet
Coding, for multi-alphabet memoryless sources. It is appropriate in situations where
only a small, unknown subset of the possible alphabet symbols can be expected to
occur in any particular data sequence. We provide a competitive analysis which shows
that the performance of Sparse Sequential Dirichlet Coding will be close to that of
a Sequential Dirichlet Coder that knows in advance the exact subset of occurring
alphabet symbols. Empirically we show that our technique can perform similarly to
the more computationally demanding Sequential Sub-Alphabet Estimator, while using
less computational resources.
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Restricted until
2037-12-31
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