Succinct nearest neighbor search
By Eric Sadit Tellez, Edgar Chávez, Gonzalo Navarro. 2011
42 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Retrieval & Search, Algorithms & Complexity, Signal Processing
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
- Alexander Ponomarenko
- Bilegsaikhan Naidan
- Leonid Boytsov
- Yury Malkov
- Eric Sadit Tellez
- Eric Nyberg
- Mario Graff
- Edgar Chávez
In Computer Vision and Pattern Recognition
- Alexander Ponomarenko
- Yury Malkov
- Bilegsaikhan Naidan
- Leonid Boytsov
- Eric Sadit Tellez
- Eric Nyberg
- Vladimir V. Krylov
- Andrey Logvinov
In Information Retrieval & Search
- Alexander Ponomarenko
- Yury Malkov
- Leonid Boytsov
- Bilegsaikhan Naidan
- Eric Sadit Tellez
- Edgar Chávez
- Mario Graff
- Vladimir V. Krylov
In Algorithms & Complexity
- Alexander Ponomarenko
- Yury Malkov
- Edgar Chávez
- Vladimir V. Krylov
- Bilegsaikhan Naidan
- Leonid Boytsov
- Eric Sadit Tellez
- Andrey Logvinov
In Signal Processing
- Alexander Ponomarenko
- Yury Malkov
- Leonid Boytsov
- Bilegsaikhan Naidan
- Eric Sadit Tellez
- Edgar Chávez
- Mario Graff
- Eric Nyberg
Citations in the corpus
- Singleton indexes for nearest neighbor search (2016, unknown): “Examples of approximate techniques are [27, 7, 13, 1, 16, 15].”
- Comparative Analysis of Data Structures for Approximate Nearest Neighbor Search (2014, methodology): “Then, we associate these numPrefix closest pivots with the data point via an inverted file [23].”
- Permutation Search Methods are Efficient, Yet Faster Search is Possible (2015, methodology): “NAPP is a neighborhood approximation index described in § 2 [40].”
- Near neighbor searching with K nearest references (2015, methodology): “CNAPP simply uses simCNAPP ¼ jset ðûÞ \ setðv̂Þj, which takes values in ½0‥K .”
- Approximate nearest neighbor algorithm based on navigable small world graphs (2014, methodology): “To get an idea about how the algorithm performs compared to previous k-NN algorithms, we have run a test from [25], a 10 million entries subset of CoPHiR collection [38].”