Efficient large-scale sequence comparison by locality-sensitive hashing
By Jeremy D. Buhler. 2001
264 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Herv'e J'egou
- Charu C. Aggarwal
- J. Urgen Schmidhuber
- Cordelia Schmid
- Rajeev Motwani
- Matthijs Douze
- Moses Charikar
- Sariel Har-Peled
In Molecular Biology
- Heng Li
- Sergey Koren
- Adam M. Phillippy
- Steven L. Salzberg
- Richard Durbin
- Konstantin Berlin
- Jue Ruan
- Eamonn J. Keogh
Citations in the corpus
- Deep learning in neural networks: An overview (2014, methodology): “It outperformed previous searchers for similar documents, such as Locality Sensitive Hashing (Buhler, 2001; Datar, Immorlica, Indyk, & Mirrokni, 2004).”
- Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality (2012, background): “Lower bounds for such embeddings have been investigated as well [KN05].”
- Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search (2007, background): “1-approximation in [13], and as many as 583 hash tables are used in [6].”
- Assembling large genomes with single-molecule sequencing and locality-sensitive hashing (2014, background): “…have been successfully Assembling large genomes with single-molecule sequencing and locality-sensitive hashing Konstantin Berlin 1–3,6 , Sergey Koren 4, 6 , Chen-Shan A rt i c l e s applied to document similarity 20 , image similarity 22 , sequence similarity 23–25 and metagenomic clustering 26 .”
- Google news personalization: scalable online collaborative filtering (2007, unknown): “[13, 9, 5].”
- Probabilistic discovery of time series motifs (2003, methodology): “Following [34, 4 ], we observe that given two randomly-generated words of size w over an alphabet of size a, the probability that they match with up to d errors is”
- Data clustering: 50 years beyond K-means (2008, unknown): “, Locality Sensitive Hash (Buhler, 2001)).”
- Locality-sensitive hashing scheme based on p-stable distributions (2004, background): “This is due to the fact that, in many cases, approximate nearest neighbor is almost as good as the exact one; in particular, if the distance measure accurately captures the notion of user quality, then small differences in the distance should not matter.”
- Finding Motifs Using Random Projections (2001, unknown): “[11], and in computational biology by Buhler [7].”
- Mapping short DNA sequencing reads and calling variants using mapping quality scores. (2008, background): “The six hash tables correspond to six noncontiguous seed templates (Buhler 2001; Ma et al. 2002).”
- PatternHunter: faster and more sensitive homology search (2002, methodology): “It has been brought to our attention, that a related but conceptually different approach, has been applied to ungapped homology search in Buhler (2001) , and Califano and Rigoutsos (1995).”