Title :
Forecasting and discriminant analysis
Author :
Gud, Anastasiya ; Shatovska, Tetyana
Author_Institution :
Anastasiya Gud - Software Dept., Kharkiv Nat. Univ. of Radioelectron., Kharkov, Ukraine
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. This paper points out that LSI ignores discrimination while concentrating on representation.
Keywords :
covariance matrices; indexing; text analysis; document representation; document-term matrix; eigendecomposition; latent semantic indexing; linear discriminant analysis; statistical classification methods; text classification; Covariance matrix; Data mining; Eigenvalues and eigenfunctions; Histograms; Indexing; Large scale integration; Linear discriminant analysis; Pattern recognition; Principal component analysis; Training data; Latent Semantic Indexing; linear discriminant analysis; text classification;
Conference_Titel :
CAD Systems in Microelectronics, 2009. CADSM 2009. 10th International Conference - The Experience of Designing and Application of
Conference_Location :
Lviv-Polyana
Print_ISBN :
978-966-2191-05-9
