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Improving PPM with dynamic parameter updates


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Publication Date: 2015

Journal Title: Data Compression Conference 2015

Publisher: IEEE

Pages: 193-202

Language: English

Type: Article

Metadata: Show full item record

Citation: Steinruecken, C., Ghahramani, Z., & MacKay, D. (2015). Improving PPM with dynamic parameter updates. Data Compression Conference 2015, 193-202.

Description: This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/DCC.2015.77

Abstract: This article makes several improvements to the classic PPM algorithm, resulting in a new algorithm with superior compression effectiveness on human text. The key differences of our algorithm to classic PPM are that (A) rather than the original escape mechanism, we use a generalised blending method with explicit hyper-parameters that control the way symbol counts are combined to form predictions; (B) different hyper-parameters are used for classes of different contexts; and (C) these hyper-parameters are updated dynamically using gradient information. The resulting algorithm (PPM-DP) compresses human text better than all currently published variants of PPM, CTW, DMC, LZ, CSE and BWT, with runtime only slightly slower than classic PPM.

Keywords: PPM, blending, data compression, dynamic updates, escape mechanism, gradients

Identifiers:

This record's URL: http://dx.doi.org/10.1109/DCC.2015.77https://www.repository.cam.ac.uk/handle/1810/254106





Author: Steinruecken, ChristianGhahramani, ZoubinMacKay, David

Source: https://www.repository.cam.ac.uk/handle/1810/254106



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