数据压缩中的混合策略
Mixing Strategies in Data Compression
摘要：
我们提出几何加权作为一种在数据压缩中组合多个模型的新方法。我们的研究结果揭示了PAQ加权背后的基本原理，并将其推广到非二进制字母表。基于类似的技术，我们提出了一个新的，通用的线性混合技术。所有新的混合技术都依赖于给定的权重向量。我们考虑的问题，找到最佳的权重和重量优化导致一个严格凸（因此，良好的性质）的优化问题。最后，实验评估比较了两个提出的混合技术的二进制字母表。结果表明，几何加权法优于线性加权法。
英文摘要：
We propose geometric weighting as a novel method to combine multiple models in data compression. Our results reveal the rationale behind PAQ-weighting and generalize it to a non-binary alphabet. Based on a similar technique we present a new, generic linear mixture technique. All novel mixture techniques rely on given weight vectors. We consider the problem of finding optimal weights and show that the weight optimization leads to a strictly convex (and thus, good-natured) optimization problem. Finally, an experimental evaluation compares the two presented mixture techniques for a binary alphabet. The results indicate that geometric weighting is superior to linear weighting.
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