SLSB-forest:approximate k nearest neighbors searching on high dimensional data
The study of approximate k nearest neighbors query has attracted broad attention.Local sensitive hash is one of the mainstream ways to solve this problem.Local sensitive hash and its varients have noted the following problems:the uneven distribution of hashed data in the buckets,it cannot calculate...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2017-09-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017193/ |
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| author | Tu QIAN Jiangbo QIAN Yihong DONG Huahui CHEN |
| author_facet | Tu QIAN Jiangbo QIAN Yihong DONG Huahui CHEN |
| author_sort | Tu QIAN |
| collection | DOAJ |
| description | The study of approximate k nearest neighbors query has attracted broad attention.Local sensitive hash is one of the mainstream ways to solve this problem.Local sensitive hash and its varients have noted the following problems:the uneven distribution of hashed data in the buckets,it cannot calculate the appropriate query range (for the parameter k) to build index.To tackle the above problem,a new data struct which called SLSB-forest was presented.The SLSB-forest combined the LSH and the B-tree to maintain the amount of bucket’s data in reasonable range.Two query algorithms were proposed:fast and accurate priority search.Theory and experimental results prove that query range can dynamic change during searching approximate k nearest neighbors. |
| format | Article |
| id | doaj-art-a6be95eabe084bcc9cde30daf7acea4d |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2017-09-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-a6be95eabe084bcc9cde30daf7acea4d2025-01-15T03:06:10ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012017-09-0133586859600081SLSB-forest:approximate k nearest neighbors searching on high dimensional dataTu QIANJiangbo QIANYihong DONGHuahui CHENThe study of approximate k nearest neighbors query has attracted broad attention.Local sensitive hash is one of the mainstream ways to solve this problem.Local sensitive hash and its varients have noted the following problems:the uneven distribution of hashed data in the buckets,it cannot calculate the appropriate query range (for the parameter k) to build index.To tackle the above problem,a new data struct which called SLSB-forest was presented.The SLSB-forest combined the LSH and the B-tree to maintain the amount of bucket’s data in reasonable range.Two query algorithms were proposed:fast and accurate priority search.Theory and experimental results prove that query range can dynamic change during searching approximate k nearest neighbors.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017193/approximate k nearest neighborlocality sensitive hashhigh dimensionality |
| spellingShingle | Tu QIAN Jiangbo QIAN Yihong DONG Huahui CHEN SLSB-forest:approximate k nearest neighbors searching on high dimensional data Dianxin kexue approximate k nearest neighbor locality sensitive hash high dimensionality |
| title | SLSB-forest:approximate k nearest neighbors searching on high dimensional data |
| title_full | SLSB-forest:approximate k nearest neighbors searching on high dimensional data |
| title_fullStr | SLSB-forest:approximate k nearest neighbors searching on high dimensional data |
| title_full_unstemmed | SLSB-forest:approximate k nearest neighbors searching on high dimensional data |
| title_short | SLSB-forest:approximate k nearest neighbors searching on high dimensional data |
| title_sort | slsb forest approximate k nearest neighbors searching on high dimensional data |
| topic | approximate k nearest neighbor locality sensitive hash high dimensionality |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017193/ |
| work_keys_str_mv | AT tuqian slsbforestapproximateknearestneighborssearchingonhighdimensionaldata AT jiangboqian slsbforestapproximateknearestneighborssearchingonhighdimensionaldata AT yihongdong slsbforestapproximateknearestneighborssearchingonhighdimensionaldata AT huahuichen slsbforestapproximateknearestneighborssearchingonhighdimensionaldata |