Combinatorial PCA and SVM methods for feature selection in learning classifications (applications to text categorization)

@article{Anghelescu2003CombinatorialPA,
  title={Combinatorial PCA and SVM methods for feature selection in learning classifications (applications to text categorization)},
  author={Andrei Anghelescu and Ilya B. Muchnik},
  journal={IEMC '03 Proceedings. Managing Technologically Driven Organizations: The Human Side of Innovation and Change (IEEE Cat. No.03CH37502)},
  year={2003},
  pages={491-496}
}
We describe a purely combinatorial approach of obtaining meaningful representations of text data. More precisely, we describe two different methods that materialize this approach: we call them combinatorial principal component analysis (cPCA) and combinatorial support vector machines (cSVM). These names emphasise mathematical analogies between the well known PCA and SVM, on one hand, and our respective methods. For evaluating the selected spaces of features, we used the environment set for TREC… CONTINUE READING

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