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• W2652783164 abstract "To be tractable and robust to data noise, existing metric learning algorithms commonly rely on PCA as a pre-processing step. How can we know, however, that PCA, or any other specific dimensionality reduction technique, is the method of choice for the problem at hand? The answer is simple: We cannot! To address this issue, in this paper, we develop a Riemannian framework to jointly learn a mapping performing dimensionality reduction and a metric in the induced space. Our experiments evidence that, while we directly work on high-dimensional features, our approach yields competitive runtimes with and higher accuracy than state-of-the-art metric learning algorithms." @default.
• W2652783164 created "2017-06-30" @default.
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• W2652783164 date "2017-08-06" @default.
• W2652783164 modified "2023-09-26" @default.
• W2652783164 title "Joint dimensionality reduction and metric learning: a geometric take" @default.
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