The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Random projection trees and low dimensional manifolds
Abstract
We present a simple variant of the k-d tree which automatically adapts to intrinsic low dimensional structure in data without having to explicitly learn this structure.
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Cites
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Belkin, M., & Niyogi, P. (2003) · Neural Computation · doi:10.1162/089976603321780317 · Cited by 7,621
Multidimensional binary search trees used for associative searching
Bentley, J. L. (1975) · Communications of the ACM · doi:10.1145/361002.361007 · Cited by 7,520
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Candes, E. J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.885507 · Cited by 6,839
Neural Information Processing Systems
(2018) · The Deep Learning Revolution · doi:10.7551/mitpress/11474.003.0014 · Cited by 3,730
A Probabilistic Theory of Pattern Recognition
Devroye, L., Györfi, L., & Lugosi, G. (1996) · Stochastic Modelling and Applied Probability · doi:10.1007/978-1-4612-0711-5 · Cited by 3,274
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Cited by
Algorithms for Reinforcement Learning
Szepesvári, C. (2010) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.1007/978-3-031-01551-9 · Cited by 1,049
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Hie, B., Bryson, B., & Berger, B. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0113-3 · Cited by 974
Signal Processing With Compressive Measurements
Davenport, M. A., Boufounos, P. T., Wakin, M. B., & Baraniuk, R. G. (2010) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2009.2039178 · Cited by 623
Testing the manifold hypothesis
Fefferman, C., Mitter, S., & Narayanan, H. (2016) · Journal of the American Mathematical Society · doi:10.1090/jams/852 · Cited by 430
Survey of Vector Database Management Systems
Pan, J. J., Wang, J., & Li, G. (2023) · arXiv · doi:10.48550/arxiv.2310.14021 · Cited by 184
Metadata
- Authors
- Dasgupta, S., & Freund, Y.
- Venue
- Proceedings of the fortieth annual ACM symposium on Theory of computing
- Publisher
- ACM
- Published
- 2008-05-17
- Pages
- 537-546
- OpenAlex
- W2118123209
- Cited by
- 375
- References
- 25
- FWCI
- 10.67
- Type
- conference paper
- Language
- en
- Open access
- closed
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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.
