A ranking hashing algorithm based on listwise supervision
Recently,learning to hash technology has been used for the similarity search of large-scale data.It can simultaneous increase the search speed and reduce the storage cost through transforming the data into binary codes.At present,most ranking hashing algorithms compare the consistency of data in the...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2019-05-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019072/ |
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| author | Anbang YANG Jiangbo QIAN Yihong DONG Huahui CHEN |
| author_facet | Anbang YANG Jiangbo QIAN Yihong DONG Huahui CHEN |
| author_sort | Anbang YANG |
| collection | DOAJ |
| description | Recently,learning to hash technology has been used for the similarity search of large-scale data.It can simultaneous increase the search speed and reduce the storage cost through transforming the data into binary codes.At present,most ranking hashing algorithms compare the consistency of data in the Euclidean space and the Hamming space to construct the loss function.However,because the Hamming distance is a discrete integer value,there may be many data points sharing the same Hamming distance result in the exact ranking cannot be performed.To address this challenging issue,the encoded data was divided into several subspaces with the same length.Each subspace was set with different weights.The Hamming distance was calculated according to different subspace weights.The experimental results show that this algorithm can effectively sort the data in the Hamming space and improve the accuracy of the query compared with other learning to hash algorithms. |
| format | Article |
| id | doaj-art-761648b5f364484b9cfc5c37da7fddcc |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2019-05-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-761648b5f364484b9cfc5c37da7fddcc2025-01-15T03:02:55ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012019-05-0135788559589820A ranking hashing algorithm based on listwise supervisionAnbang YANGJiangbo QIANYihong DONGHuahui CHENRecently,learning to hash technology has been used for the similarity search of large-scale data.It can simultaneous increase the search speed and reduce the storage cost through transforming the data into binary codes.At present,most ranking hashing algorithms compare the consistency of data in the Euclidean space and the Hamming space to construct the loss function.However,because the Hamming distance is a discrete integer value,there may be many data points sharing the same Hamming distance result in the exact ranking cannot be performed.To address this challenging issue,the encoded data was divided into several subspaces with the same length.Each subspace was set with different weights.The Hamming distance was calculated according to different subspace weights.The experimental results show that this algorithm can effectively sort the data in the Hamming space and improve the accuracy of the query compared with other learning to hash algorithms.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019072/learning to hashsimilarity searchranking hashingsubspaces with different weights |
| spellingShingle | Anbang YANG Jiangbo QIAN Yihong DONG Huahui CHEN A ranking hashing algorithm based on listwise supervision Dianxin kexue learning to hash similarity search ranking hashing subspaces with different weights |
| title | A ranking hashing algorithm based on listwise supervision |
| title_full | A ranking hashing algorithm based on listwise supervision |
| title_fullStr | A ranking hashing algorithm based on listwise supervision |
| title_full_unstemmed | A ranking hashing algorithm based on listwise supervision |
| title_short | A ranking hashing algorithm based on listwise supervision |
| title_sort | ranking hashing algorithm based on listwise supervision |
| topic | learning to hash similarity search ranking hashing subspaces with different weights |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019072/ |
| work_keys_str_mv | AT anbangyang arankinghashingalgorithmbasedonlistwisesupervision AT jiangboqian arankinghashingalgorithmbasedonlistwisesupervision AT yihongdong arankinghashingalgorithmbasedonlistwisesupervision AT huahuichen arankinghashingalgorithmbasedonlistwisesupervision AT anbangyang rankinghashingalgorithmbasedonlistwisesupervision AT jiangboqian rankinghashingalgorithmbasedonlistwisesupervision AT yihongdong rankinghashingalgorithmbasedonlistwisesupervision AT huahuichen rankinghashingalgorithmbasedonlistwisesupervision |