Improving PPM with Dynamic Parameter Updates

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.