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• W101693742 abstract "Document representation using the bag-of-words approach may require bringing the dimensionality of the representation down in order to be able to make effective use of various statistical classification methods. Latent Semantic Indexing (LSI) is one such method that is based on eigendecomposition of the covariance of the document-term matrix. Another often used approach is to select a small number of most important features out of the whole set according to some relevant criterion. This paper points out that LSI ignores discrimination while concentrating on representation. Furthermore, selection methods fail to produce a feature set that jointly optimizes class discrimination. As a remedy, we suggest supervised linear discriminative transforms, and report good classification results applying these to the Reuters-21578 database." @default.
• W101693742 created "2016-06-24" @default.
• W101693742 creator A5038258675 @default.
• W101693742 date "2007-01-01" @default.
• W101693742 modified "2023-09-27" @default.
• W101693742 title "Linear Discriminant Analysis in Document Classification" @default.
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