Document Classification after Dimension Reduction through a Modified Gram-Schmidt Process

This paper proposes a modified Gram-Schmidt algorithm for dimension reduction of a document vector space in order to do classification. We also evaluate the performance of the proposed algorithm by comparing with commonly known algorithms such as the centroid-based algorithm and latent semantic inde...

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Bibliographic Details
Published inWireless Networks and Computational Intelligence pp. 236 - 243
Main Authors Guha, Sumanta, Lamichhane, Ananta Raj
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesCommunications in Computer and Information Science
Subjects
Centroid
Dimension Reduction
Document Classification
Gram-Schmidt
LSI
Vector Space Model
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Summary:This paper proposes a modified Gram-Schmidt algorithm for dimension reduction of a document vector space in order to do classification. We also evaluate the performance of the proposed algorithm by comparing with commonly known algorithms such as the centroid-based algorithm and latent semantic indexing. Further, to measure efficiency, a modified Gram-Schmidt training set is applied in the centroid-based algorithm and latent semantic indexing as well. Performance measurement was based on two different parameters, viz., classification accuracy and closeness of similarity with the corresponding training set. The results shows that modified Gram-Schmidt algorithm is indeed an effective method for dimension reduction prior classification. Moreover, it is easy to code and computationally inexpensive.
ISBN:3642316859
9783642316852
ISSN:1865-0929
1865-0937
DOI:10.1007/978-3-642-31686-9_28