Dictionary Learning from Ambiguously Labeled Data

Yi-Chen Chen, Vishal M. Patel, Jaishanker K. Pillai, Rama Chellappa, P. J. Phillips

CVPR 2013

doi:10.1109/CVPR.2013.52 /cvpr/2013/chen2013cvpr-dictionary/

Abstract

We propose a novel dictionary-based learning method for ambiguously labeled multiclass classification, where each training sample has multiple labels and only one of them is the correct label. The dictionary learning problem is solved using an iterative alternating algorithm. At each iteration of the algorithm, two alternating steps are performed: a confidence update and a dictionary update. The confidence of each sample is defined as the probability distribution on its ambiguous labels. The dictionaries are updated using either soft (EM-based) or hard decision rules. Extensive evaluations on existing datasets demonstrate that the proposed method performs significantly better than state-of-the-art ambiguously labeled learning approaches.

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Cite

Text

Chen et al. "Dictionary Learning from Ambiguously Labeled Data." Conference on Computer Vision and Pattern Recognition, 2013. doi:10.1109/CVPR.2013.52

Markdown

[Chen et al. "Dictionary Learning from Ambiguously Labeled Data." Conference on Computer Vision and Pattern Recognition, 2013.](https://mlanthology.org/cvpr/2013/chen2013cvpr-dictionary/) doi:10.1109/CVPR.2013.52

BibTeX

@inproceedings{chen2013cvpr-dictionary,
  title     = {{Dictionary Learning from Ambiguously Labeled Data}},
  author    = {Chen, Yi-Chen and Patel, Vishal M. and Pillai, Jaishanker K. and Chellappa, Rama and Phillips, P. J.},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2013},
  doi       = {10.1109/CVPR.2013.52},
  url       = {https://mlanthology.org/cvpr/2013/chen2013cvpr-dictionary/}
}