Lossless Compression Based on the Sequence Memoizer

In this work we describe a sequence compression method based on combining a Bayesian nonparametric sequence model with entropy encoding. The model, a hierarchy of Pitman-Yor processes of unbounded depth previously proposed by Wood et al. [16] in the context of language modelling, allows modelling of long-range dependencies by allowing conditioning contexts of unbounded length. We show that incremental approximate inference can be performed in this model, thereby allowing it to be used in a text compression setting. The resulting compressor reliably outperforms several PPM variants on many types of data, but is particularly effective in compressing data that exhibits power law properties.

• Type: Conference
• Year: 2010
• Where: DCC
• Authors: Jan Gasthaus, Frank Wood, Yee Whye Teh
• Keywords: Computer Graphics, DCC 2010, Nonparametric Sequence Model, Sequence Compression Method, Unbounded Depth