Robust Regression on Image Manifolds for Ordered Label Denoising

CVPR 2015

doi:10.1109/CVPR.2015.7298627 /cvpr/2015/wu2015cvpr-robust/

Abstract

In this paper, we present a computationally efficient and non-parametric method for robust regression on manifolds. We apply our algorithm to the problem of correcting mislabeled examples from image collections with ordered (e.g., real-valued, ordinal) labels. Compared to related methods for robust regression, our method achieves superior denoising accuracy on a variety of data sets, with label corruption levels as high as 80%. For a diverse set of widely-used, large-scale, publicly-available data sets, our approach results in image labels that more accurately describe the associated images.

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Cite

Text

Wu and Souvenir. "Robust Regression on Image Manifolds for Ordered Label Denoising." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7298627

Markdown

[Wu and Souvenir. "Robust Regression on Image Manifolds for Ordered Label Denoising." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/wu2015cvpr-robust/) doi:10.1109/CVPR.2015.7298627

BibTeX

@inproceedings{wu2015cvpr-robust,
  title     = {{Robust Regression on Image Manifolds for Ordered Label Denoising}},
  author    = {Wu, Hui and Souvenir, Richard},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2015},
  doi       = {10.1109/CVPR.2015.7298627},
  url       = {https://mlanthology.org/cvpr/2015/wu2015cvpr-robust/}
}