The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Database-friendly random projections
Abstract
A classic result of Johnson and Lindenstrauss asserts that any set of n points in d-dimensional Euclidean space can be embedded into k-dimensional Euclidean space where k is logarithmic in n and independent of d so that all pairwise distances are maintained within an arbitrarily small factor. All known constructions of such embeddings involve projecting the n points onto a random k-dimensional hyperplane. We give a novel construction of the embedding, suitable for database applications, which amounts to computing a simple aggregate over k random attribute partitions.
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Cites
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Johnson, W. B., & Lindenstrauss, J. (1984) · Contemporary Mathematics · doi:10.1090/conm/026/737400 · Cited by 2,523
The geometry of graphs and some of its algorithmic applications
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Learning mixtures of Gaussians
Dasgupta, S. (2003) · 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039) · doi:10.1109/sffcs.1999.814639 · Cited by 588
Two algorithms for nearest-neighbor search in high dimensions
Kleinberg, J. M. (1997) · Proceedings of the twenty-ninth annual ACM symposium on Theory of computing - STOC '97 · doi:10.1145/258533.258653 · Cited by 348
An algorithmic theory of learning: Robust concepts and random projection
Arriaga, R. I., & Vempala, S. (2006) · Machine Learning · doi:10.1007/s10994-006-6265-7 · Cited by 316
The Johnson-Lindenstrauss lemma and the sphericity of some graphs
Frankl, P., & Maehara, H. (1988) · Journal of Combinatorial Theory, Series B · doi:10.1016/0095-8956(88)90043-3 · Cited by 299
Stable distributions, pseudorandom generators, embeddings and data stream computation
Indyk, P. (2002) · Proceedings 41st Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.2000.892082 · Cited by 238
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Sanjeev, A., & Kannan, R. (2001) · Proceedings of the thirty-third annual ACM symposium on Theory of computing · doi:10.1145/380752.380808 · Cited by 216
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A Simple Proof of the Restricted Isometry Property for Random Matrices
Baraniuk, R., Davenport, M., DeVore, R., & Wakin, M. (2008) · Constructive Approximation · doi:10.1007/s00365-007-9003-x · Cited by 2,372
Challenges of Big Data analysis
Fan, J., Han, F., & Liu, H. (2014) · National Science Review · doi:10.1093/nsr/nwt032 · Cited by 1,482
A survey on unsupervised outlier detection in high‐dimensional numerical data
Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11161 · Cited by 864
Finding Frequent Items in Data Streams
Charikar, M., Chen, K., & Farach-Colton, M. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45465-9_59 · Cited by 650
Outlier Detection: Methods, Models, and Classification
Boukerche, A., Zheng, L., & Alfandi, O. (2021) · ACM Computing Surveys · doi:10.1145/3381028 · Cited by 334
Metadata
- Authors
- Achlioptas, D.
- Venue
- Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
- Publisher
- ACM
- Published
- 2001-05
- Pages
- 274-281
- OpenAlex
- W2053171205
- Cited by
- 699
- References
- 11
- FWCI
- 5.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Achlioptas, D. (2001). Database-friendly random projections. Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. https://doi.org/10.1145/375551.375608
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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