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• W3098466109 abstract "In many learning tasks, the structure of the target space of a function holds rich information about the relationships between evaluations of functions on different data points. Existing approaches attempt to exploit this relationship information implicitly by enforcing smoothness on function evaluations only. However, what happens if we explicitly regularize the relationships between function evaluations? Inspired by homophily, we regularize based on a smooth relationship function, either defined from the data or with labels. In experiments, we demonstrate that this significantly improves the performance of state-of-the-art algorithms in semi-supervised classification and in spectral data embedding for constrained clustering and dimensionality reduction." @default.
• W3098466109 created "2020-11-23" @default.
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• W3098466109 date "2015-06-01" @default.
• W3098466109 modified "2023-10-18" @default.
• W3098466109 title "Semi-supervised learning with explicit relationship regularization" @default.
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• W3098466109 doi "https://doi.org/10.1109/cvpr.2015.7298831" @default.
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