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Description
  • A fast algorithm to solve weighted L1-minimization problems with N N square “measuring matrices is proposed. The method is recursive-in-model-order and tracks a homotopy path that goes through solutions of the optimization sub-tasks in the order of 1 through N. It thus yields solutions for all model orders and performs this task faster than the other compared methods. We show applications of this method in sparse linear system identification, in particular, the estimation of sparse target-cancellation filters for audio source separation.
  • A fast algorithm to solve weighted L1-minimization problems with N N square “measuring matrices is proposed. The method is recursive-in-model-order and tracks a homotopy path that goes through solutions of the optimization sub-tasks in the order of 1 through N. It thus yields solutions for all model orders and performs this task faster than the other compared methods. We show applications of this method in sparse linear system identification, in particular, the estimation of sparse target-cancellation filters for audio source separation. (en)
Title
  • A Homotopy Recursive-in-Model-Order Algorithm for Weighted LASSO
  • A Homotopy Recursive-in-Model-Order Algorithm for Weighted LASSO (en)
skos:prefLabel
  • A Homotopy Recursive-in-Model-Order Algorithm for Weighted LASSO
  • A Homotopy Recursive-in-Model-Order Algorithm for Weighted LASSO (en)
skos:notation
  • RIV/67985556:_____/14:00427989!RIV15-AV0-67985556
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  • I
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  • 780
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  • RIV/67985556:_____/14:00427989
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  • Sparse Linear Regression; homotopy; Levinson-Durbin algorithm (en)
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  • [8D7D287702DA]
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  • Florencie
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  • Piscataway
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  • 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
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  • Koldovský, Zbyněk
  • Tichavský, Petr
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number of pages
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  • IEEE
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  • 978-1-4799-2892-7
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