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- W2124789966 abstract "We initiate a study comparing effectiveness of the transformed spaces learned by recently proposed supervised, and semi-supervised metric learning algorithms to those generated by previously proposed unsupervised dimensionality reduction methods (e.g., PCA). Through a variety of experiments on different real-world datasets, we find IDML-IT, a semi-supervised metric learning algorithm to be the most effective." @default.
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- W2124789966 date "2010-07-11" @default.
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- W2124789966 title "Learning Better Data Representation Using Inference-Driven Metric Learning" @default.
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