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Application of a non-linear dimension reduction algorithm on document clustering

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  • Received:2007-09-03 Revised:1900-01-01 Online:2008-02-01 Published:2008-02-01
  • Contact: Yue-Heng SUN

非线性维数约减算法在文档聚类中的应用

孙越恒 侯越先 何丕廉   

  1. 天津大学 天津大学 天津大学
  • 通讯作者: 孙越恒

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.

Key words: non-linear dimension reduction, linear dimension reduction, self-organizing isometric embedding, document clustering

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。

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