Document Classification Via TextCC Based on Stereographic Projection

Author

Zhang, Zhen-ya ; Zhang, Shu-guang ; Wang, Xu-fa

Author_Institution

Microsoft Key Lab. of Multimedia Comput. & Commun., Univ. of Sci. & Technol. of China, Hefei

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

1368

Lastpage

1372

Abstract

TextCC can classify real documents instantly by cosine similarity. In this paper, stereographic projection is defined from n dimensional real space to the surface of the unit sphere in (n+1) dimensional space. This paper also proposes the relation between the Euclidean distance in n dimensional space and the cosine similarity in (n+1) dimensional real space. To classify documents with represented vectors normalized by stereographic projection, modification on the construction of the weight matrix of hidden layer of TextCC and the fundamental for those modifications are presented. With those modifications, TextCC can classify real documents instantly by Euclidean distance. Experimental results show that TextCC can classify real documents well by Euclidean distance based on stereographic projection

Keywords

classification; learning (artificial intelligence); matrix algebra; text analysis; vectors; Euclidean distance; TextCC training; automatic text classification; cosine similarity; document classification; stereographic projection; vectors; weight matrix; Computer science; Cybernetics; Electronic mail; Euclidean distance; Feeds; Frequency; Laboratories; Machine learning; Multimedia computing; Neural networks; Text categorization; Vocabulary; Stereographic projection; TextCC; cosine similarity;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258706

Filename

4028277