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Knowledge Extraction with Non-Negative Matrix Factorization for Text Classification
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| Text classification has received increasing interest over the past decades for its wide range of applications driven by the ubiquity of textual information. The high dimensionality of those applications led to pervasive use of dimensionality reduction methods, often black-box feature extraction non-linear techniques. We show how Non-Negative Matrix Factorization (NMF), an algorithm able to learn a parts-based representation of data by imposing non-negativity constraints, can be used to represent and extract knowledge from a text classification problem. The resulting reduced set of features is tested with kernel-based machines on Reuters-21578 benchmark showing the method's performance competitiveness. | 281.59 KB | Adobe PDF |
Orientador(es)
Resumo(s)
Text classification has received increasing interest over the past decades for its wide range of applications driven by the ubiquity of textual information. The high dimensionality of those applications led to pervasive use of dimensionality reduction methods, often black-box feature extraction non-linear techniques. We show how Non-Negative Matrix Factorization (NMF), an algorithm able to learn a parts-based representation of data by imposing non-negativity constraints, can be used to represent and extract knowledge from a text classification problem. The resulting reduced set of features is tested with kernel-based machines on Reuters-21578 benchmark showing the method's performance competitiveness.
Descrição
10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009, 23 September 2009 through 26 September 2009 - Code 79260
Palavras-chave
Support Vector Machine Semantic Feature Nonnegative Matrix Factorization Positive Matrix Factorization Knowledge Extraction
Contexto Educativo
Citação
Silva, C., Ribeiro, B. (2009). Knowledge Extraction with Non-Negative Matrix Factorization for Text Classification. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_37.
Editora
Springer Nature
Coleções
Licença CC
Sem licença CC