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Low mutual and average coherence dictionary learning using convex approximation

Parsa, J ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/ICASSP40776.2020.9052901
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. In dictionary learning, a desirable property for the dictionary is to be of low mutual and average coherences. Mutual coherence is defined as the maximum absolute correlation between distinct atoms of the dictionary, whereas the average coherence is a measure of the average correlations. In this paper, we consider a dictionary learning problem regularized with the average coherence and constrained by an upper-bound on the mutual coherence of the dictionary. Our main contribution is then to propose an algorithm for solving the resulting problem based on convexly approximating the cost function over the dictionary. Experimental results demonstrate that the proposed approach has higher convergence rate and lower representation error (with a fixed sparsity parameter) than other methods, while yielding similar mutual and average coherence values. © 2020 IEEE
  6. Keywords:
  7. Average coherence ; Compressed sensing ; Dictionary learning ; Mutual coherence ; Sparse coding ; Cost functions ; Speech communication ; Absolute correlations ; Algorithm for solving ; Average coherences ; Convergence rates ; Convex approximation ; Dictionary learning ; Mutual coherence ; Problem-based ; Audio signal processing
  8. Source: 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, 4 May 2020 through 8 May 2020 ; Volume 2020-May , 2020 , Pages 3417-3421
  9. URL: https://ieeexplore.ieee.org/document/9052901