Semi-supervised learning by constructing query-document heterogeneous information network
Various graph-based algorithms for semi-supervised learning have been proposed in recent literatures. How-ever, although classification on homogeneous networks has been studied for decades, classification on heterogeneous networks has not been explored until recently. The semi-supervised classificat...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2014-08-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.08.006/ |
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| _version_ | 1841539223762501632 |
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| author | Yu-feng LIU Ren-fa LI |
| author_facet | Yu-feng LIU Ren-fa LI |
| author_sort | Yu-feng LIU |
| collection | DOAJ |
| description | Various graph-based algorithms for semi-supervised learning have been proposed in recent literatures. How-ever, although classification on homogeneous networks has been studied for decades, classification on heterogeneous networks has not been explored until recently. The semi-supervised classification problem on query-document heteroge-neous information network which incorporate the bipartite graph with the content information from both sides is consid-ered. In order to strengthen the network structure, class information of sample nodes is introduced. A semi-supervised learning algorithm based on two frameworks including the novel graph-based regularization framework and the iterative framework is investigated. In the regularization framework, a new cost function to consider the direct relationship be-tween two entity sets and the content information from both sides which leads to a significant improvement over the baseline methods is developed. Experimental results demonstrate that proposed method achieves the best performance with consistent and promising improvements. |
| format | Article |
| id | doaj-art-ac977f8034814127a8aeee6de07b41b1 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2014-08-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-ac977f8034814127a8aeee6de07b41b12025-01-14T07:25:14ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2014-08-0135404759683223Semi-supervised learning by constructing query-document heterogeneous information networkYu-feng LIURen-fa LIVarious graph-based algorithms for semi-supervised learning have been proposed in recent literatures. How-ever, although classification on homogeneous networks has been studied for decades, classification on heterogeneous networks has not been explored until recently. The semi-supervised classification problem on query-document heteroge-neous information network which incorporate the bipartite graph with the content information from both sides is consid-ered. In order to strengthen the network structure, class information of sample nodes is introduced. A semi-supervised learning algorithm based on two frameworks including the novel graph-based regularization framework and the iterative framework is investigated. In the regularization framework, a new cost function to consider the direct relationship be-tween two entity sets and the content information from both sides which leads to a significant improvement over the baseline methods is developed. Experimental results demonstrate that proposed method achieves the best performance with consistent and promising improvements.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.08.006/heterogeneous information networkssemi-supervised learninginformation retrievalclick-through data |
| spellingShingle | Yu-feng LIU Ren-fa LI Semi-supervised learning by constructing query-document heterogeneous information network Tongxin xuebao heterogeneous information networks semi-supervised learning information retrieval click-through data |
| title | Semi-supervised learning by constructing query-document heterogeneous information network |
| title_full | Semi-supervised learning by constructing query-document heterogeneous information network |
| title_fullStr | Semi-supervised learning by constructing query-document heterogeneous information network |
| title_full_unstemmed | Semi-supervised learning by constructing query-document heterogeneous information network |
| title_short | Semi-supervised learning by constructing query-document heterogeneous information network |
| title_sort | semi supervised learning by constructing query document heterogeneous information network |
| topic | heterogeneous information networks semi-supervised learning information retrieval click-through data |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.08.006/ |
| work_keys_str_mv | AT yufengliu semisupervisedlearningbyconstructingquerydocumentheterogeneousinformationnetwork AT renfali semisupervisedlearningbyconstructingquerydocumentheterogeneousinformationnetwork |