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• W3142476504 abstract "As annotations of data can be scarce in large-scale practical problems, leveraging unlabelled examples is one of the most important aspects of machine learning. This is the aim of semi-supervised learning. To benefit from the access to unlabelled data, it is natural to diffuse smoothly knowledge of labelled data to unlabelled one. This induces to the use of Laplacian regularization. Yet, current implementations of Laplacian regularization suffer from several drawbacks, notably the well-known curse of dimensionality. In this paper, we provide a statistical analysis to overcome those issues, and unveil a large body of spectral filtering methods that exhibit desirable behaviors. They are implemented through (reproducing) kernel methods, for which we provide realistic computational guidelines in order to make our method usable with large amounts of data." @default.
• W3142476504 created "2021-04-13" @default.
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• W3142476504 date "2021-12-06" @default.
• W3142476504 modified "2023-10-17" @default.
• W3142476504 title "Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning" @default.
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