A convergent incoherent dictionary learning algorithm for sparse coding

Chenglong Bao Department of Mathematics, National University of Singapore, Singapore Yuhui Quan Department of Mathematics, National University of Singapore, Singapore Hui Ji Department of Mathematics, National University of Singapore, Singapore

Machine Learning mathscidoc:2206.41008

ECCV, 2014.7
Recently, sparse coding has been widely used in many applications ranging from image recovery to pattern recognition. The low mutual coherence of a dictionary is an important property that ensures the optimality of the sparse code generated from this dictionary. Indeed, most existing dictionary learning methods for sparse coding either implicitly or explicitly tried to learn an incoherent dictionary, which requires solving a very challenging non-convex optimization problem. In this paper, we proposed a hybrid alternating proximal algorithm for incoherent dictionary learning, and established its global convergence property. Such a convergent incoherent dictionary learning method is not only of theoretical interest, but also might benefit many sparse coding based applications.
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@inproceedings{chenglong2014a,
  title={A convergent incoherent dictionary learning algorithm for sparse coding},
  author={Chenglong Bao, Yuhui Quan, and Hui Ji},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220616154333252468380},
  booktitle={ECCV},
  year={2014},
}
Chenglong Bao, Yuhui Quan, and Hui Ji. A convergent incoherent dictionary learning algorithm for sparse coding. 2014. In ECCV. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20220616154333252468380.
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