Dimensionality Reduction by Semantic Mapping in Text Categorization
Identifieur interne : 000B13 ( Main/Exploration ); précédent : 000B12; suivant : 000B14Dimensionality Reduction by Semantic Mapping in Text Categorization
Auteurs : Renato Fernandes Corrêa [Brésil] ; Teresa Bernarda Ludermir [Brésil]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2004.
English descriptors
- Teeft :
- Algorithm, Categorization, Classification error, Data vectors, Dimensionality, Dimensionality reduction, Document maps, Document vectors, Feature extraction method, Future works, Good alternative, Information retrieval, International conference, Mapping, Mapping method, Matrix, Model vectors, Mutual similarities, Neural networks, Node, Original feature, Original features, Original space, Projection matrix, Random mapping, Renato fernandes, Semantic, Semantic mapping, Semantic maps, Standard deviation, Teresa bernarda ludermir, Text categorization, Text categorization tasks, Websom project.
Abstract
Abstract: In text categorization tasks, the dimensionality reduction become necessary to computation and interpretability of the results generated by machine learning algorithms due to the high-dimensional vector representation of the documents. This paper describes a new feature extraction method called semantic mapping and its application in categorization of web documents. The semantic mapping uses SOM maps to construct variables in reduced space, where each variable describes the behavior of a group of features semantically related. The performance of the semantic mapping is measured and compared empirically with the performance of sparse random mapping and PCA methods and shows to be better than random mapping and a good alternative to PCA.
Url:
DOI: 10.1007/978-3-540-30499-9_160
Affiliations:
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This paper describes a new feature extraction method called semantic mapping and its application in categorization of web documents. The semantic mapping uses SOM maps to construct variables in reduced space, where each variable describes the behavior of a group of features semantically related. 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