Nearest neighbor search algorithm for high dimensional data based on weighted self-taught hashing
Because of efficiency in query and storage,learning hash is applied in solving the nearest neighbor search problem.The learning hash usually converts high-dimensional data into binary codes.In this way,the similarities between binary codes from two objects are conserved as they were in the original...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2017-06-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017100/ |
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| author | Cong PENG Jiangbo QIAN Huahui CHEN Yihong DONG |
| author_facet | Cong PENG Jiangbo QIAN Huahui CHEN Yihong DONG |
| author_sort | Cong PENG |
| collection | DOAJ |
| description | Because of efficiency in query and storage,learning hash is applied in solving the nearest neighbor search problem.The learning hash usually converts high-dimensional data into binary codes.In this way,the similarities between binary codes from two objects are conserved as they were in the original high-dimensional space.In practical applications,a lot of data which have the same distance from the query point but with different code will be returned.How to reorder these candidates is a problem.An algorithm named weighted self-taught hashing was proposed.Experimental results show that the proposed algorithm can reorder the different binary codes with the same Hamming distances efficiently.Compared to the naive algorithm,the F1-score of the proposed algorithm is improved by about 2 times and it is better than the homologous algorithms,furthermore,the time cost is reduced by an order of magnitude. |
| format | Article |
| id | doaj-art-733e127c973e469fbd036ab975408ba2 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2017-06-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-733e127c973e469fbd036ab975408ba22025-01-15T03:12:42ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012017-06-0133738559601759Nearest neighbor search algorithm for high dimensional data based on weighted self-taught hashingCong PENGJiangbo QIANHuahui CHENYihong DONGBecause of efficiency in query and storage,learning hash is applied in solving the nearest neighbor search problem.The learning hash usually converts high-dimensional data into binary codes.In this way,the similarities between binary codes from two objects are conserved as they were in the original high-dimensional space.In practical applications,a lot of data which have the same distance from the query point but with different code will be returned.How to reorder these candidates is a problem.An algorithm named weighted self-taught hashing was proposed.Experimental results show that the proposed algorithm can reorder the different binary codes with the same Hamming distances efficiently.Compared to the naive algorithm,the F1-score of the proposed algorithm is improved by about 2 times and it is better than the homologous algorithms,furthermore,the time cost is reduced by an order of magnitude.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017100/nearest neighbor searchlearning hashweighted self-taughthigh-dimensional data |
| spellingShingle | Cong PENG Jiangbo QIAN Huahui CHEN Yihong DONG Nearest neighbor search algorithm for high dimensional data based on weighted self-taught hashing Dianxin kexue nearest neighbor search learning hash weighted self-taught high-dimensional data |
| title | Nearest neighbor search algorithm for high dimensional data based on weighted self-taught hashing |
| title_full | Nearest neighbor search algorithm for high dimensional data based on weighted self-taught hashing |
| title_fullStr | Nearest neighbor search algorithm for high dimensional data based on weighted self-taught hashing |
| title_full_unstemmed | Nearest neighbor search algorithm for high dimensional data based on weighted self-taught hashing |
| title_short | Nearest neighbor search algorithm for high dimensional data based on weighted self-taught hashing |
| title_sort | nearest neighbor search algorithm for high dimensional data based on weighted self taught hashing |
| topic | nearest neighbor search learning hash weighted self-taught high-dimensional data |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2017100/ |
| work_keys_str_mv | AT congpeng nearestneighborsearchalgorithmforhighdimensionaldatabasedonweightedselftaughthashing AT jiangboqian nearestneighborsearchalgorithmforhighdimensionaldatabasedonweightedselftaughthashing AT huahuichen nearestneighborsearchalgorithmforhighdimensionaldatabasedonweightedselftaughthashing AT yihongdong nearestneighborsearchalgorithmforhighdimensionaldatabasedonweightedselftaughthashing |