The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Searching in metric spaces
Open accessAbstract
The problem of searching the elements of a set that are close to a given query element under some similarity criterion has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. We are interested in the rather general case where the similarity criterion defines a metric space, instead of the more restricted case of a vector space. Many solutions have been proposed in different areas, in many cases without cross-knowledge. Because of this, the same ideas have been reconceived several times, and very different presentations have been given for the same approaches. We present some basic results that explain the intrinsic difficulty of the search problem. This includes a quantitative definition of the elusive concept of "intrinsic dimensionality." We also present a unified view of all the known proposals to organize metric spaces, so as to be able to understand them under a common framework. Most approaches turn out to be variations on a few different concepts. We organize those works in a taxonomy that allows us to devise new algorithms from combinations of concepts not noticed before because of the lack of communication between different communities. We present experiments validating our results and comparing the existing approaches. We finish with recommendations for practitioners and open questions for future development.
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Cites
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Malkov, Y., Ponomarenko, A., Logvinov, A., & Krylov, V. (2014) · Information Systems · doi:10.1016/j.is.2013.10.006 · Cited by 419
Finding nearest neighbors in growth-restricted metrics
Karger, D. R., & Ruhl, M. (2002) · Proceedings of the thiry-fourth annual ACM symposium on Theory of computing · doi:10.1145/509907.510013 · Cited by 334
A Complexity-Invariant Distance Measure for Time Series
Batista, G. E. A. P. A., Wang, X., & Keogh, E. J. (2011) · Proceedings of the 2011 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972818.60 · Cited by 329
Paulovich, F. V., Nonato, L. G., Minghim, R., & Levkowitz, H. (2008) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2007.70443 · Cited by 316
A PCA-based similarity measure for multivariate time series
Yang, K., & Shahabi, C. (2004) · Proceedings of the 2nd ACM international workshop on Multimedia databases · doi:10.1145/1032604.1032616 · Cited by 276
Metadata
- Authors
- Chávez, E., Navarro, G., Baeza-Yates, R., & Marroquín, J. L.
- Venue
- ACM Computing Surveys
- Published
- 2001-09
- Volume
- 33
- Issue
- 3
- Pages
- 273-321
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2038044292
- Cited by
- 1,246
- References
- 51
- FWCI
- 61.64
- Type
- journal article
- Language
- en
- Open access
- green
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- Chávez, E., Navarro, G., Baeza-Yates, R., & Marroquín, J. L. (2001). Searching in metric spaces. ACM Computing Surveys. https://doi.org/10.1145/502807.502808
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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.
