Forecasting and discriminant analysis

Author

Gud, Anastasiya ; Shatovska, Tetyana

Author_Institution

Anastasiya Gud - Software Dept., Kharkiv Nat. Univ. of Radioelectron., Kharkov, Ukraine

fYear

2009

fDate

24-28 Feb. 2009

Firstpage

536

Lastpage

538

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;

fLanguage

English

Publisher

ieee

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

Type

conf

Filename

4839910

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=492950