fast exact k nearest neighbors search using an orthogonal search tree
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
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
PR 2010
國立雲林科技大學National Yunlin University of Science and Technology
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Outline
Motivation Objective Methodology Experiments Conclusion Comments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.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
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Motivation
For a big PAT(Principal Axis Search), the computation time to evaluate boundary points and projection values will be large.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.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.
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Intelligent Database Systems Lab
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The OST(orthogonal search tree) algorithm OST construction process
K Nearest neighbors
search using the OST
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Intelligent Database Systems Lab
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The OST construction process
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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 4,5,6 7,8,9
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Intelligent Database Systems Lab
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I. M.Methodology
K nearest neighbors search
using the orthogonal search tree
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1,2,3,4,5,6,7,8,9
1,2,3 4,5,6 7,8,9
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Intelligent Database Systems Lab
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Example 1 Uniform Markov source
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Intelligent Database Systems Lab
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Intelligent Database Systems Lab
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Example 2 auto-correlated data
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Intelligent Database Systems Lab
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Example 3 Clustered Gaussian data
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Intelligent Database Systems Lab
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Example 4 Data sets are codebook
generated using 6 real images.
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Intelligent Database Systems Lab
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Example 5 Statlog data set.
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34% 39%
Intelligent Database Systems Lab
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I. M.Conclusion
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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).
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Comments
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Advantage To reduce the computation of the kNN finding process.
Drawback Lack of illustrations
Application Classification