The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
LSH forest: self-tuning indexes for similarity search
Abstract
We consider the problem of indexing high-dimensional data for answering (approximate) similarity-search queries. Similarity indexes prove to be important in a wide variety of settings: Web search engines desire fast, parallel, main-memory-based indexes for similarity search on text data; database systems desire disk-based similarity indexes for high-dimensional data, including text and images; peer-to-peer systems desire distributed similarity indexes with low communication cost. We propose an indexing scheme called LSH Forest which is applicable in all the above contexts. Our index uses the well-known technique of locality-sensitive hashing (LSH), but improves upon previous designs by (a) eliminating the different data-dependent parameters for which LSH must be constantly hand-tuned, and (b) improving on LSH's performance guarantees for skewed data distributions while retaining the same storage and query overhead. We show how to construct this index in main memory, on disk, in parallel systems, and in peer-to-peer systems. We evaluate the design with experiments on multiple text corpora and demonstrate both the self-tuning nature and the superior performance of LSH Forest.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
R-trees: a dynamic index structure for spatial searching
Guttman, A. (1984) · Proceedings of the 1984 ACM SIGMOD international conference on Management of data - SIGMOD '84 · doi:10.1145/602259.602266 · Cited by 6,580
Approximate nearest neighbors: towards removing the curse of dimensionality
Indyk, P., & Motwani, R. (1998) · Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98 · doi:10.1145/276698.276876 · Cited by 4,188
The R*-tree: an efficient and robust access method for points and rectangles
Beckmann, N., Kriegel, H.-P., Schneider, R., & Seeger, B. (1990) · Proceedings of the 1990 ACM SIGMOD international conference on Management of data · doi:10.1145/93597.98741 · Cited by 4,161
(1999) · Cited by 3,186
Authoritative sources in a hyperlinked environment
Kleinberg, J. M. (1998) · Symposium on Discrete Algorithms · doi:10.5555/314613.315045 · Cited by 1,814
On the resemblance and containment of documents
Broder, A. Z. (2002) · Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No.97TB100171) · doi:10.1109/sequen.1997.666900 · Cited by 1,698
Syntactic clustering of the Web
Broder, A. Z., Glassman, S. C., Manasse, M. S., & Zweig, G. (1997) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(97)00031-7 · Cited by 1,343
Berchtold, S., Keim, D. A., & Kriegel, H.-P. (2002) · Readings in Multimedia Computing and Networking · doi:10.1016/b978-155860651-7/50124-8 · Cited by 1,118
PATRICIA—Practical Algorithm To Retrieve Information Coded in Alphanumeric
Morrison, D. R. (1968) · Journal of the ACM · doi:10.1145/321479.321481 · Cited by 969
Faloutsos, C., & Lin, K.-I. (1995) · Proceedings of the 1995 ACM SIGMOD international conference on Management of data - SIGMOD '95 · doi:10.1145/223784.223812 · Cited by 897
Cited by
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,613
LDAHash: Improved Matching with Smaller Descriptors
Strecha, C., Bronstein, A. M., Bronstein, M. M., & Fua, P. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.103 · Cited by 605
One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome
Capecchi, A., Probst, D., & Reymond, J.-L. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00445-4 · Cited by 363
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
Rae, J. W., Hunt, J. J., Harley, T., Danihelka, I., Senior, A., Wayne, G., Graves, A., & Lillicrap, T. P. (2016) · arXiv · doi:10.48550/arxiv.1610.09027 · Cited by 89
Principles of hash-based text retrieval
Stein, B. (2007) · Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1277741.1277832 · Cited by 55
Metadata
- Authors
- Bawa, M., Condie, T., & Ganesan, P.
- Venue
- Proceedings of the 14th international conference on World Wide Web - WWW '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 651
- OpenAlex
- W2148781362
- Cited by
- 353
- References
- 26
- Type
- conference abstract
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: LSH forest: self-tuning indexes for similarity search. https://app.cochat.ai/papers/10.1145/1060745.1060840
- The paper
- Bawa, M., Condie, T., & Ganesan, P. (2005). LSH forest: self-tuning indexes for similarity search. Proceedings of the 14th international conference on World Wide Web - WWW '05. https://doi.org/10.1145/1060745.1060840
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
