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A Comparison of SVD,SVR,ADE and IRR for Latent Semantic Indexing

机译:SVD,SVR,ADE和IRR用于潜在语义索引的比较

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摘要

Recently,singular value decomposition (SVD) and its variants,which are singular value rescaling (SVR),approximation dimension equalization (ADE) and iterative residual rescaling (IRR),were proposed to conduct the job of latent semantic indexing (LSI). Although they are all based on linear algebraic method for tern-document matrix computation,which is SVD,the basic motivations behind them concerning LSI are different from each other.In this paper,a series of experiments are conducted to examine their effectiveness of LSI for the practical application of text mining,including information retrieval,text categorization and similarity measure. The experimental results demonstrate that SVD and SVR have better performances than other proposed LSI methods in the above mentioned applications.Meanwhile,ADE and IRR,because of the too much difference between their approximation matrix and original termdocument matrix in Frobenius norm,can not derive good performances for text mining applications using LSI.
机译:近年来,提出了奇异值分解(SVD)及其变体,即奇异值重新缩放(SVR),近似维均衡(ADE)和迭代残差重新缩放(IRR),以进行潜在语义索引(LSI)的工作。尽管它们都是基于线性代数方法进行SVD​​的三元文档矩阵计算,但是它们背后关于LSI的基本动机却彼此不同。本文进行了一系列实验,以检验LSI在LSI中的有效性。文本挖掘的实际应用包括信息检索,文本分类和相似性度量。实验结果表明,在上述应用中,SVD和SVR的性能优于其他拟议的LSI方法。同时,ADE和IRR由于Frobenius范数中的近似矩阵与原始项文档矩阵之间的差异太大,因此无法得出很好的结果。 LSI的文本挖掘应用程序的性能。

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