Latent Semantic Indexing based on Simple Principal Component Analysis

机译：基于简单主成分分析的潜在语义索引

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摘要

In this paper, we propose to use the Simple Principal Component Analysis (SPCA) for dimensionality reduction of the vector space information retrieval model. The SPCA algorithm is a data-oriented fast method which does not require the computation of the variance-covariance matrix. In SPCA principal components are estimated iteratively so we also propose a criteria to determine the convergence. The optimum number of iterations for each principal component can be detem-ined using the criteria. Experimentally, we show that the SPCA-based method offers improvement over the conventional SVD-based method despite its small amount of computation.

机译：在本文中，我们建议使用简单主成分分析（SPCA）减少向量空间信息检索模型的维数。 SPCA算法是一种面向数据的快速方法，不需要计算方差-协方差矩阵。在SPCA中，主要成分是迭代估计的，因此我们还提出了确定收敛的标准。可以使用准则确定每个主成分的最佳迭代次数。实验表明，尽管计算量很小，但基于SPCA的方法仍比基于SVD的常规方法有所改进。

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来源
《The Second International Conference on Information Jul 24-27, 2002 Beijing, China》|2002年|p.172-177|共6页
会议地点 Beijing(CN)
作者
Shingo Kuroiwa; Satoru Tsuge; Hironori Tani; Masami Shishibori; Fuji Ren; Kenji Kita;
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作者单位

Depertment of Information Sciences and Intelligent Systems, Faculty of Engineering The University of Tokushima, Minami-Josanjima, Tokushima 770-8506, Japan;

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会议组织
原文格式 PDF
正文语种 eng
中图分类 C9;
关键词
information retrieval; LSI; SVD; PCA;

机译：信息检索; LSI; SVD; PCA;

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Latent Semantic Indexing based on Simple Principal Component Analysis

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