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      Structured least squares problems and robust estimators

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      Author
      Pilanci, M.
      Arıkan, Orhan
      Pinar, M. C.
      Date
      2010-10-22
      Source Title
      IEEE Transactions on Signal Processing
      Print ISSN
      1053-587X
      Publisher
      IEEE
      Volume
      58
      Issue
      5
      Pages
      2453 - 2465
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      A novel approach is proposed to provide robust and accurate estimates for linear regression problems when both the measurement vector and the coefficient matrix are structured and subject to errors or uncertainty. A new analytic formulation is developed in terms of the gradient flow of the residual norm to analyze and provide estimates to the regression. The presented analysis enables us to establish theoretical performance guarantees to compare with existing methods and also offers a criterion to choose the regularization parameter autonomously. Theoretical results and simulations in applications such as blind identification, multiple frequency estimation and deconvolution show that the proposed technique outperforms alternative methods in mean-squared error for a significant range of signal-to-noise ratio values.
      Keywords
      Blind identification
      Deconvolution
      Errors-in-variables
      Frequency estimation
      Least squares
      Robust least squares
      Structured total least squares
      Blind identification
      Blind identifications
      Errors in variables
      Least Square
      Robust least squares
      Structured total least squares
      Blind equalization
      Communication channels (information theory)
      Convolution
      Estimation
      Measurement errors
      Signal to noise ratio
      Uncertainty analysis
      Frequency estimation
      Permalink
      http://hdl.handle.net/11693/22338
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TSP.2010.2041279
      Collections
      • Department of Electrical and Electronics Engineering 3524
      • Department of Industrial Engineering 677
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