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Learning overcomplete dictionaries from markovian data

Akhavan, S ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1109/SAM.2018.8448625
  3. Publisher: IEEE Computer Society , 2018
  4. Abstract:
  5. We explore the dictionary learning problem for sparse representation when the signals are dependent. In this paper, a first-order Markovian model is considered for dependency of the signals, that has many applications especially in medical signals. It is shown that the considered dependency among the signals can degrade the performance of the existing dictionary learning algorithms. Hence, we propose a method using the Maximum Log-likelihood Estimator (MLE) and the Expectation Minimization (EM) algorithm to learn the dictionary from the signals generated under the first-order Markovian model. Simulation results show the efficiency of the proposed method in comparison with the state-of-the-art algorithms. © 2018 IEEE
  6. Keywords:
  7. Dictionary ; Sparse representation ; Array processing ; Glossaries ; Markov processes ; Dictionary learning algorithms ; Expectation-minimization algorithm ; Markov matrix ; Markovian model ; Over-complete dictionaries ; State ; State-of-the-art algorithms ; Learning algorithms
  8. Source: 10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018, 8 July 2018 through 11 July 2018 ; Volume 2018-July , 2018 , Pages 218-222 ; 2151870X (ISSN); 9781538647523 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/8448625