The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The Similarity Metric
Abstract
A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied. We propose a new "normalized information distance," based on the noncomputable notion of Kolmogorov complexity, and show that it is in this class and it minorizes every computable distance in the class (that is, it is universal in that it discovers all computable similarities). We demonstrate that it is a metric and call it the similarity metric . This theory forms the foundation for a new practical tool. To evidence generality and robustness, we give two distinctive applications in widely divergent areas using standard compression programs like gzip and GenCompress. First, we compare whole mitochondrial genomes and infer their evolutionary history. This results in a first completely automatic computed whole mitochondrial phylogeny tree. Secondly, we fully automatically compute the language tree of 52 different languages.
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Cites
Genome-scale approaches to resolving incongruence in molecular phylogenies.
Rokas, A., Williams, B. L., King, N., & Carroll, S. B. (2003) · Nature · doi:10.1038/nature02053 · Cited by 1,512
Cited by
Simplicity: a unifying principle in cognitive science?
Chater, N., & Vitányi, P. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(02)00005-0 · Cited by 494
Labbé, C., & Labbé, D. (2013) · Scientometrics · doi:10.1007/s11192-012-0781-y · Cited by 132
Efficient DNA sequence compression with neural networks
Silva, M., Pratas, D., & Pinho, A. J. (2020) · GigaScience · doi:10.1093/gigascience/giaa119 · Cited by 57
Compression ensembles quantify aesthetic complexity and the evolution of visual art
Karjus, A., Canet Solà, M., Ohm, T., Ahnert, S. E., & Schich, M. (2023) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00397-3 · Cited by 32
Predicting phenotype transition probabilities via conditional algorithmic probability approximations
Dingle, K., Novev, J. K., Ahnert, S. E., & Louis, A. A. (2022) · Journal of the Royal Society Interface · doi:10.1098/rsif.2022.0694 · Cited by 13
Metadata
- Authors
- Li, M., Chen, X., Li, X., Ma, B., & Vitanyi, P. M. B.
- Venue
- IEEE Transactions on Information Theory
- Published
- 2004-12
- Volume
- 50
- Issue
- 12
- Pages
- 3250-3264
- ISSN
- 0018-9448 (ISSN-L)
- OpenAlex
- W2144221002
- Cited by
- 1,062
- References
- 1
- FWCI
- 36.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, M., Chen, X., Li, X., Ma, B., & Vitanyi, P. M. B. (2004). The Similarity Metric. IEEE Transactions on Information Theory. https://doi.org/10.1109/tit.2004.838101
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
