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Robust Sequential Learning Algorithms for Linear Observation Models.
- Source :
-
IEEE Transactions on Signal Processing . Jun2007 Part 1, Vol. 55 Issue 6, p2472-2485. 14p. 4 Charts, 9 Graphs. - Publication Year :
- 2007
-
Abstract
- This paper presents a study of sequential parameter estimation based on a linear non-Gaussian observation model. To develop robust algorithms, we consider a family of heavy-tailed distributions that can be expressed as the scale mixture of Gaussian and extend the development to include some robust penalty functions. We treat the problem as a Bayesian learning problem and develop an iterative algorithm by using the Laplace approximation for the posterior and the minorization-maximization (MM) algorithm as an optimization tool. We then study a one-step implementation of the iterative algorithm. This leads to a family of generalized robust RLS-type of algorithms which include several well-known algorithms as special cases. Using a further simplification that the covariance is fixed, leads to a family of generalized robust LMS-type of algorithms. Through mathematical analysis and simulations, we demonstrate the robustness of these algorithms [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 1053587X
- Volume :
- 55
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 52037362
- Full Text :
- https://doi.org/10.1109/TSP.2007.893733