In this paper, we propose a novel way to include unsupervised feature selection methods in probabilistic taxonomy learning models. We leverage on the computation of logistic regression to exploit unsupervised feature selection of singular value decomposition (SVD). Experiments show that this way of using SVD for feature selection positively affects performances.
Fallucchi, F., Zanzotto, F.m. (2009). Singular value decomposition for Feature Selection in Taxonomy Learning. In Proceedings of the Conference on Recent Advances on Natural Language Processing. John Benjamins.
Singular value decomposition for Feature Selection in Taxonomy Learning
FALLUCCHI, FRANCESCA;ZANZOTTO, FABIO MASSIMO
2009-01-01
Abstract
In this paper, we propose a novel way to include unsupervised feature selection methods in probabilistic taxonomy learning models. We leverage on the computation of logistic regression to exploit unsupervised feature selection of singular value decomposition (SVD). Experiments show that this way of using SVD for feature selection positively affects performances.File in questo prodotto:
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