Trainlets: Dictionary Learning in High Dimensions

@article{Sulam2016TrainletsDL,
  title={Trainlets: Dictionary Learning in High Dimensions},
  author={Jeremias Sulam and Boaz Ophir and Michael Zibulevsky and Michael Elad},
  journal={IEEE Transactions on Signal Processing},
  year={2016},
  volume={64},
  pages={3180-3193}
}
Sparse representation has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance. Combined with the ability to learn a dictionary from signal examples, sparsity-inspired algorithms are often achieving state-of-the-art results in a wide variety of tasks. These methods have traditionally been restricted to small dimensions mainly due to the computational constraints that the dictionary learning problem entails. In the… CONTINUE READING
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