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Fast exact k nearest neighbors search using an orthogonal search tree

Fast exact k nearest neighbors search using an orthogonal search tree. Presenter : Chun-Ping Wu Authors :Yi- Ching Liaw , Maw-Lin Leou , Chien -Min Wu. 國立雲林科技大學 National Yunlin University of Science and Technology. PR 2010. Outline. Motivation Objective Methodology Experiments

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Fast exact k nearest neighbors search using an orthogonal search tree

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  1. Fast exact k nearest neighbors search using an orthogonal search tree Presenter : Chun-Ping Wu Authors :Yi-ChingLiaw, Maw-Lin Leou, Chien-Min Wu 國立雲林科技大學 National Yunlin University of Science and Technology PR 2010

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. Motivation • The finding process of k nearest neighbors for a query point using FSA(full search algorithm) is very time consuming. • Many algorithms want to reduce the computational complexity of the kNN finding process. • Pre-created tree structure

  4. Motivation • For a big PAT(Principal Axis Search), the computation time to evaluate boundary points and projection values will be large.

  5. Objective • To reduce the computation time on evaluation boundary points and projection values in the kNN searching process for a query point. • The proposed method requires no boundary points and only little computation time on evaluating projection values in the kNN finding process.

  6. Methodology • The OST(orthogonal search tree) algorithm • OST construction process • K Nearest neighbors search using the OST

  7. Methodology • The OST construction process 1,2,3, 4,5,6, 7,8,9 1,2,3, 4,5,6, 7,8,9 1,2,3, 4,5,6, 7,8,9 1,2,3 4,5,6 7,8,9 1 2 3 1,2,3 4,5,6 7,8,9

  8. Methodology • K nearest neighbors search using the orthogonal search tree 1,2,3, 4,5,6, 7,8,9 1,2,3 4,5,6 7,8,9 1 2 3 4 5 6 7 8 9

  9. Experiments • Example1 • Uniform Markov source

  10. Experiments

  11. Experiments • Example 2 • auto-correlated data

  12. Experiments • Example 3 • Clustered Gaussian data

  13. Experiments • Example 4 • Data sets are codebook generated using 6 real images.

  14. Experiments • Example 5 • Statlog data set. 34% 39%

  15. Conclusion • Experimental results show that the proposed method always spends less computation time to find the kNN for a query point than the other methods. • The proposed method will find the same results as those of the FSA(full search algorithm). 15

  16. Comments • Advantage • To reduce the computation ofthe kNN finding process. • Drawback • Lack of illustrations • Application • Classification 16

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