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SPICE and LIKES :
SPICE and LIKES : Two hyperparameter-free methods for sparse-parameter estimation
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- Stoica, Peter (author)
- Uppsala universitet,Avdelningen för systemteknik,Reglerteknik
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- Babu, Prabhu (author)
- Uppsala universitet,Avdelningen för systemteknik,Reglerteknik
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(creator_code:org_t)
- Elsevier BV, 2012
- 2012
- English.
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In: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 92:7, s. 1580-1590
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- SPICE (SParse Iterative Covariance-based Estimation) is a recently introduced method for sparse-parameter estimation in linear models using a robust covariance fitting criterion that does not depend on any hyperparameters. In this paper we revisit the derivation of SPICE to streamline it and to provide further insights into this method. LIKES (LIKelihood-based Estimation of Sparse parameters) is a new method obtained in a hyperparameter-free manner from the maximum-likelihood principle applied to the same estimation problem as considered by SPICE. Both SPICE and LIKES are shown to provide accurate parameter estimates even from scarce data samples, with LIKES being more accurate than SPICE at the cost of an increased computational burden.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Keyword
- Scarce data
- Sparse parameter estimation methods
- Robust covariance fitting
- Maximum-likelihood method
- SDP
- SOCP
- Spectral analysis
- Range-Doppler imaging
Publication and Content Type
- ref (subject category)
- art (subject category)
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