FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset. FLANN is written in C++ and contains bindings for the following languages: C, MATLAB and Python.

References in zbMATH (referenced in 33 articles )

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  1. Bahmani, Bahador; Sun, WaiChing: A kd-tree-accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data (2021)
  2. Eggersmann, Robert; Stainier, Laurent; Ortiz, Michael; Reese, Stefanie: Efficient data structures for model-free data-driven computational mechanics (2021)
  3. Giffon, Luc; Emiya, Valentin; Kadri, Hachem; Ralaivola, Liva: Quick-means: accelerating inference for K-means by learning fast transforms (2021)
  4. Li, Yuliang; Wang, Jianguo; Pullman, Benjamin; Bandeira, Nuno; Papakonstantinou, Yannis: Index-based, high-dimensional, cosine threshold querying with optimality guarantees (2021)
  5. Ma, Hengzhao; Li, Jianzhong: A sub-linear time algorithm for approximating k-nearest-neighbor with full quality guarantee (2021)
  6. Wang, Bao; Osher, Stan J.: Graph interpolating activation improves both natural and robust accuracies in data-efficient deep learning (2021)
  7. Tremblay, Nicolas; Loukas, Andreas: Approximating spectral clustering via sampling: a review (2020)
  8. Chen, Yewang; Zhou, Lida; Tang, Yi; Singh, Jai Puneet; Bouguila, Nizar; Wang, Cheng; Wang, Huazhen; Du, Jixiang: Fast neighbor search by using revised (k)-d tree (2019)
  9. Geng, Zhiqiang; Meng, Qingchao; Bai, Ju; Chen, Jie; Han, Yongming; Wei, Qin; Ouyang, Zhi: A model-free Bayesian classifier (2019)
  10. Jaeger, Manfred; Lippi, Marco; Pellegrini, Giovanni; Passerini, Andrea: Counts-of-counts similarity for prediction and search in relational data (2019)
  11. Long, Andrew W.; Ferguson, Andrew L.: Landmark diffusion maps (L-dMaps): accelerated manifold learning out-of-sample extension (2019)
  12. Łukasik, Szymon; Lalik, Konrad; Sarna, Piotr; Kowalski, Piotr A.; Charytanowicz, Małgorzata; Kulczycki, Piotr: Efficient astronomical data condensation using approximate nearest neighbors (2019)
  13. Stainier, Laurent; Leygue, Adrien; Ortiz, Michael: Model-free data-driven methods in mechanics: material data identification and solvers (2019)
  14. Tremblay, Nicolas; Barthelmé, Simon; Amblard, Pierre-Olivier: Determinantal point processes for coresets (2019)
  15. Yazdanbakhsh, Omolbanin; Dick, Scott: FANCFIS: fast adaptive neuro-complex fuzzy inference system (2019)
  16. Keivani, Omid; Sinha, Kaushik; Ram, Parikshit: Improved maximum inner product search with better theoretical guarantee using randomized partition trees (2018)
  17. Liu, Yingfan; Wei, Hao; Cheng, Hong: Exploiting lower bounds to accelerate approximate nearest neighbor search on high-dimensional data (2018)
  18. Memon, Kashif Hussain; Lee, Dong-Ho: Generalised kernel weighted fuzzy c-means clustering algorithm with local information (2018)
  19. Rachkovskij, D. A.: Index structures for fast similarity search for real vectors. II (2018)
  20. Rachkovskij, D. A.: Index structures for fast similarity search for real-valued vectors. I (2018)

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Further publications can be found at: http://www.cs.ubc.ca/research/flann/#publications