潜在语义索引中快速更新算法
Fast updating algorithms for latent semantic indexing
AI总结：
本文提出基于Rayleigh-Ritz方法的SVD更新算法，通过子空间投影降低计算和存储成本，实验显示其在较低计算成本下保持与标准方法相当的精度。
摘要：
本文讨论了在信息检索的潜在语义索引（LSI）方法中用于更新近似奇异值分解（SVD）的几种算法。考虑了一个基于Rayleigh-Ritz投影方法的统一框架。首先讨论了SVD的Rayleigh-Ritz方法，并用此观点解释了Zha-Simon算法。这种观点导致了几种替代方法，旨在通过利用较小维度子空间的投影技术来降低计算成本和存储需求。数值实验表明，所提出的算法在较低的计算成本下，其精度与标准方法相当。
英文摘要：
This paper discusses a few algorithms for updating the approximate Singular Value Decomposition (SVD) in the context of information retrieval by Latent Semantic Indexing (LSI) methods. A unifying framework is considered which is based on Rayleigh-Ritz projection methods. First, a Rayleigh-Ritz approach for the SVD is discussed and it is then used to interpret the Zha--Simon algorithms [SIAM J. Scient. Comput. vol. 21 (1999), pp. 782-791]. This viewpoint leads to a few alternatives whose goal is to reduce computational cost and storage requirement by projection techniques that utilize subspaces of much smaller dimension. Numerical experiments show that the proposed algorithms yield accuracies comparable to those obtained from standard ones at a much lower computational cost.
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