A Comparison of SVD,SVR,ADE and IRR for Latent Semantic Indezing
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.
Latent Semantic Indezing Singular Value Decomposition Singular Value Rescaling Approzimation Dimension Equalization Iterative Residual Rescaling
Wen Zhang Xijin Tang Taketoshi Yoshida
School of Knowledge Science,Japan Advanced Institute of Science and Technology,1-1 Ashahidai,Tatsuno Institute of Systems Science.Academy of Mathematics and Systems Science,Chinese Academy of Sciences.
国际会议
The Ninth International Workshop on Meta-Synthesis Complez Systems(第九届综合集成与复杂国际研讨会)
成都
英文
77-85
2009-06-21（万方平台首次上网日期，不代表论文的发表时间）