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Dimensionality Reduction of very large document collections by Semantic Mapping

Corrêa, Renato Fernandes ; Ludermir, Teresa Bernarda



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Abstract:
This paper describes improving in Semantic Mapping, a feature extraction method useful to dimensionality reduction of vectors representing documents of large text collections. This method may be viewed as a specialization of the Random Mapping, method proposed in WEBSOM project. Semantic Mapping, Random Mapping and Principal Component Analysis (PCA) are applied to categorization of document collections using Self-Organizing Maps (SOM). Semantic Mapping generated document representation as good as PCA and much better than Random Mapping.


Keywords: Document Clustering, Dimensionality Reduction, Semantic Mapping
Institution: Faculty of Technology, Research Groups in Informatics
DDC classification: Data processing, computer science, computer systems

Suggested Citation:
Corrêa, Renato Fernandes ; Ludermir, Teresa Bernarda  (2007)  Dimensionality Reduction of very large document collections by Semantic Mapping.


URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2007/133



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