fast exact k nearest neighbors search using an orthogonal search tree

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

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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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I. M.Methodology

The OST(orthogonal search tree) algorithm OST construction process

K Nearest neighbors

search using the OST

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Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Methodology

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

N.Y.U.S.T.

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

N.Y.U.S.T.

I. M.Experiments

Example 1 Uniform Markov source

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Intelligent Database Systems Lab

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I. M.Experiments

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Intelligent Database Systems Lab

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I. M.Experiments

Example 2 auto-correlated data

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Intelligent Database Systems Lab

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I. M.Experiments

Example 3 Clustered Gaussian data

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Intelligent Database Systems Lab

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I. M.Experiments

Example 4 Data sets are codebook

generated using 6 real images.

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Intelligent Database Systems Lab

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I. M.Experiments

Example 5 Statlog data set.

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34% 39%

Intelligent Database Systems Lab

N.Y.U.S.T.

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