1. Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Communications of the ACM 51(1), 117–122 (2008)

   Article  Google Scholar 

2. Bentley, J.L.: Multidimensional binary search trees used for associative searching. Communications of the ACM 18(9), 509–517 (1975)

   Article MathSciNet MATH  Google Scholar 

3. Beygelzimer, A., Kakade, S., Langford, J.: Cover trees for nearest neighbor. In: Proceedings of the 23 International Conference on Machine Learning, pp. 97–104. ACM (2006)

   Google Scholar 

4. Borne, K.: Scientific data mining in astronomy, arXiv:0911.0505v1 (2009)

   Google Scholar 

5. Bustos, B., Deussen, O., Hiller, S., Keim, D.: A graphics hardware accelerated algorithm for nearest neighbor search. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006, Part IV. LNCS, vol. 3994, pp. 196–199. Springer, Heidelberg (2006)

   Google Scholar 

6. Garcia, V., Debreuve, E., Barlaud, M.: Fast k nearest neighbor search using GPU. In: CVPR Workshop on Computer Vision on GPU, Anchorage, Alaska, USA (June 2008)

   Google Scholar 

7. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn. Springer (2009)

   Google Scholar 

8. Ivezic, Z., Tyson, J.A., Acosta, E., Allsman, R., andere: Lsst: from science drivers to reference design and anticipated data products (2011)

   Google Scholar 

9. Kirk, D.B., Wen-mei, H.: Programming Massively Parallel Processors: A Hands-on Approach, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (2010)

   Google Scholar 

10. Munshi, A., Gaster, B., Mattson, T.: OpenCL Programming Guide. OpenGL Series. Addison-Wesley (2011)

    Google Scholar 

11. Nakasato, N.: Implementation of a parallel tree method on a gpu. CoRR, abs/1112.4539 (2011)

    Google Scholar 

12. nVidia Corporation. Opencl^TM best practices guide (2009),http://www.nvidia.com/content/cudazone/CUDABrowser/downloads/papers/NVIDIA_OpenCL_BestPracticesGuide.pdf

13. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)

    Google Scholar 

14. Polsterer, K.L., Zinn, P., Gieseke, F.: Finding new high-redshift quasars by asking the neighbours. Monthly Notices of the Royal Astronomical Society (MNRAS) 428(1), 226–235 (2013)

    Article  Google Scholar 

15. Shakhnarovich, G., Darrell, T., Indyk, P.: Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing). MIT Press (2006)

    Google Scholar 

16. York, D.G., et al.: The sloan digital sky survey: Technical summary. The Astronomical Journal 120(3), 1579–1587

    Google Scholar 

Download references