2004/04/20 by Alexei Kaltchenko, Kaltchenko, Alexei
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computational Complexity (cs.CC) #Computational Engineering #E.4 #FOS: Biological sciences #FOS: Computer and information sciences #Finance #Genomics (q-bio.GN) #J.3 #and Science (cs.CE) #cs.CC #cs.CE #q-bio.GN
paper · pdf · doi:10.48550/arxiv.cs/0404039
4 pages
arxiv created 2004/04/20 · arxiv updated 2009/12/01
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.