Seamlessly Integrating Effective Links with Attributes for Networked Data Classification
文献类型:会议论文
作者 | Zhao,Yangyang![]() ![]() ![]() ![]() |
出版日期 | 2015-05 |
会议日期 | May 19, 2015 - May 22, 2015 |
会议地点 | Ho Chi Minh City, Vietnam |
关键词 | Networked Data Classification Heterogeneous Information Fusion Collective Matrix Factorization |
英文摘要 |
Networked data is emerging with great amount in various fields like social networks, biological networks, research publication networks, etc. Networked data classification is therefore of critical importance in real world, and it is noticed that link information can help
improve learning performance. However, classification of such networked data can be challenging since: 1) the original links (also referred as relations) in such networks, are always sparse, incomplete and noisy; 2) it is not easy to characterize, select and leverage effective link information from the networks, involving multiple types of links with distinct
semantics; 3) it is difficult to seamlessly integrate link information with attribute information in a network. To address these limitations, in this paper we develop a novel Seamlessly-integrated Link-Attribute Collective Matrix Factorization (SLA-CMF) framework, which mines highly effective link information given arbitrary information network and leverages
it with attribute information in a unified perspective. Algorithmwise, SLA-CMF first mines highly effective link information via link path weighting and link strength learning. Then it learns a low-dimension linkattribute joint representation via graph Laplacian CMF. Finally the joint representation is put into a traditional classifier such as SVM for classification.
Extensive experiments on benchmark datasets demonstrate the effectiveness of our method. |
会议录 | Advances in Knowledge Discovery and Data Mining
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源URL | [http://ir.ia.ac.cn/handle/173211/11951] ![]() |
专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
通讯作者 | Sun,Zhengya |
作者单位 | Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Zhao,Yangyang,Sun,Zhengya,Xu,Changsheng,et al. Seamlessly Integrating Effective Links with Attributes for Networked Data Classification[C]. 见:. Ho Chi Minh City, Vietnam. May 19, 2015 - May 22, 2015. |
入库方式: OAI收割
来源:自动化研究所
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