Semi-supervised learning with explicit relationship regularization

Kim, Kwang In and Tompkin, James and Pfister, Hanspeter and Theobalt, Christian (2015) Semi-supervised learning with explicit relationship regularization. In: Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. IEEE, pp. 2188-2196.

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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.

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Contribution in Book/Report/Proceedings
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ID Code:
73210
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Deposited On:
18 Jun 2015 06:10
Refereed?:
Yes
Published?:
Published
Last Modified:
26 Sep 2020 06:53