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Updating the partial singular value decomposition in latent semantic indexing

机译:在潜在语义索引中更新部分奇异值分解

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Latent semantic indexing (LSI) is a method of information retrieval (IR) that relies heavily on the partial singular value decomposition (PSVD) of the term-document matrix representation of a data set. Calculating the PSVD of large term-document matrices is computationally expensive; hence in the case where terms or documents are merely added to an existing data set, it is extremely beneficial to update the previously calculated PSVD to reflect the changes. It is shown how updating can be used in LSI to significantly reduce the computational cost of finding the PSVD without significantly impacting performance. Moreover, it is shown how the computational cost can be reduced further, again without impacting performance, through a combination of updating and folding-in.
机译:潜在语义索引(LSI)是一种信息检索(IR)的方法,在很大程度上依赖于数据集的术语文档矩阵表示形式的部分奇异值分解(PSVD)。计算大型长期文档矩阵的PSVD的计算量很大。因此,在仅将术语或文档添加到现有数据集中的情况下,更新先前计算的PSVD以反映更改是非常有益的。它显示了如何在LSI中使用更新程序来显着降低查找PSVD的计算成本,而不会显着影响性能。此外,示出了如何通过更新和折叠的组合再次在不影响性能的情况下进一步降低计算成本。

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