期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2010
卷号：2010
出版社：ACL Anthology
摘要：We initiate a study comparing effectiveness
of the transformed spaces learned by
recently proposed supervised, and semisupervised
metric learning algorithms
to those generated by previously proposed
unsupervised dimensionality reduction
methods (e.g., PCA). Through a variety
of experiments on different realworld
datasets, we find IDML-IT, a semisupervised
metric learning algorithm to be
the most effective.
