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Dimensionality Reduction of very large document collections by Semantic Mapping
CorrĂȘa, Renato Fernandes ; Ludermir, Teresa Bernarda
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.
||Document Clustering, Dimensionality Reduction, Semantic Mapping
||Technische Fakultät, Arbeitsgruppen der Informatik
CorrĂȘa, Renato Fernandes ; Ludermir, Teresa Bernarda (2007) Dimensionality Reduction of very large document collections by Semantic Mapping.