| 非线性维数约减算法在文档聚类中的应用 |
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| 引用本文： | 孙越恒,侯越先,何丕廉.非线性维数约减算法在文档聚类中的应用[J].计算机应用,2008,28(2):488-490. |
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| 作者姓名： | 孙越恒 侯越先 何丕廉 |
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| 作者单位： | 天津大学 天津大学 天津大学 |
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| 摘 要： | 提出一种非线性维数约减算法——自组织等距嵌入实现高维文档数据的压缩，并在文档聚类实验中，与经典的线性维数约减算法—隐含语义索引进行了比较研究。实验结果表明，在复杂度显著低于LSI算法的同时，SIE算法取得了优于LSI算法的性能，且高于基准性能。
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| 关 键 词： | 非线性维数约减 线性维数约减 自组织等距嵌入 文档聚类 |
| 文章编号： | 1001-9081（2008）02-0488-03 |
| 收稿时间： | 2007-09-03 |
| 修稿时间： | 2007-11-30 |
Application of a non-linear dimension reduction algorithm on document clustering |
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| SUN Yue-heng,HOU Yue-xian,HE Pi-lian.Application of a non-linear dimension reduction algorithm on document clustering[J].journal of Computer Applications,2008,28(2):488-490. |
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| Authors: | SUN Yue-heng HOU Yue-xian HE Pi-lian |
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| Affiliation: | SUN Yue-heng,HOU Yue-xian,HE Pi-lian(School of Computer Science , Technology,Tianjin University,Tianjin 300072,China) |
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| Abstract: | This paper presented a non-linear dimension reduction algorithm-Self-organizing Isometric Embedding (SIE) to compress high-dimensional document data. The algorithm was then validated in document clustering by being compared with the typical linear dimension reduction algorithm-Latent Semantic Indexing (LSI). Experimental results show that while significantly lowering the complexity, the performance of SIE is better than that of LSI and the benchmark. |
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| Keywords: | non-linear dimension reduction linear dimension reduction self-organizing isometric embedding docunmet clustering |
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