Semi-Supervised Learning with Explicit Relationship Regularization

Kwang In Kim, James Tompkin, Hanspeter Pfister, Christian Theobalt

CVPR 2015

doi:10.1109/CVPR.2015.7298831 /cvpr/2015/kim2015cvpr-semisupervised/

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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Cite

Text

Kim et al. "Semi-Supervised Learning with Explicit Relationship Regularization." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7298831

Markdown

[Kim et al. "Semi-Supervised Learning with Explicit Relationship Regularization." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/kim2015cvpr-semisupervised/) doi:10.1109/CVPR.2015.7298831

BibTeX

@inproceedings{kim2015cvpr-semisupervised,
  title     = {{Semi-Supervised Learning with Explicit Relationship Regularization}},
  author    = {Kim, Kwang In and Tompkin, James and Pfister, Hanspeter and Theobalt, Christian},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2015},
  doi       = {10.1109/CVPR.2015.7298831},
  url       = {https://mlanthology.org/cvpr/2015/kim2015cvpr-semisupervised/}
}