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一种无监督文本特征计算模型 被引量：2

An Effective Unsupervised Feature Computing Model
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摘要 提出一种基于语义显量子勾连模型和潜量子共现模型的无监督特征提取方法,解决了当前文本聚类不支持增量式和分布式计算的问题,为后续互联网环境下海量文本聚类、单文本摘要以及多文本摘要的发展奠定了基础.实验结果表明,该模型无需领域知识库的支持,在移走约96%的冗余信息后仍能保持较好的聚类效果. This paper presents a new unsupervised feature extraction method based on the obvious quantum entangled model and the latent quantum co-occurrence model to solve the problems that current text clustering methods don＇t support incremental clustering and distributed computing,which is the foundation for the text clustering in Internet environment and single-and multi-text summary.The model without the support of domain knowledge maintains a good information clustering effect after moving ca 96% of the redundant features.Theory analysis and numerical experiments show that this model is effective.
作者 王小芳 王瑞芳 张树功
机构地区 吉林大学数学研究所 大连大学信息工程学院
出处 《吉林大学学报（理学版）》 CAS CSCD 北大核心 2010年第1期79-84,共6页 Journal of Jilin University:Science Edition
基金 国家重点基础研究发展计划973项目基金(批准号:2004CB318000)
关键词 无监督 特征提取 勾连模型 窗函数 unsupervised feature selection entangling model window function
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献10

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共引文献212

同被引文献16

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引证文献2

二级引证文献6

吉林大学学报（理学版）
2010年 第1期

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