信息距离估计算法及其在生物信息学和语言学中的应用
Algorithms for Estimating Information Distance with Application to Bioinformatics and Linguistics
摘要：
在回顾了基于Kolmogorov复杂性的不可计算概念的非归一化和归一化信息距离之后，我们讨论了如何通过数据压缩算法来近似Kolmogorov复杂性。我们认为，边信息的数据压缩的最佳算法可以成功地用于近似归一化距离。接下来，我们讨论一种基于相对熵率（也称为Kullback-Leibler发散）的替代信息距离，以及用于其估计的基于压缩的算法。基于现有的生物学和语言学数据，我们得出了意想不到的结论，即在生物信息学和计算语言学中，这种替代距离比基于柯尔莫哥洛夫复杂性的距离更相关和重要。
英文摘要：
After reviewing unnormalized and normalized information distances based on incomputable notions of Kolmogorov complexity, we discuss how Kolmogorov complexity can be approximated by data compression algorithms. We argue that optimal algorithms for data compression with side information can be successfully used to approximate the normalized distance. Next, we discuss an alternative information distance, which is based on relative entropy rate (also known as Kullback-Leibler divergence), and compression-based algorithms for its estimation. Based on available biological and linguistic data, we arrive to unexpected conclusion that in Bioinformatics and Computational Linguistics this alternative distance is more relevant and important than the ones based on Kolmogorov complexity.
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