TY - JOUR
T1 - Robust iterative fitting of multilinear models based on linear programming
AU - Vorobyov, Sergiy A.
AU - Rong, Yue
AU - Sidiropoulos, Nicholas D.
AU - Gershman, Alex B.
PY - 2004
Y1 - 2004
N2 - PARAllel FACtor (PARAFAC) analysis is an extension of low-rank matrix decomposition to higher-way arrays. It decomposes a given array in a sum of multilinear terms. PARAFAC analysis generalizes and unifies common array processing models (like joint diagonalization and ESPRIT); it has found numerous applications from blind multiuser detection and multi-dimensional harmonic retrieval, to clustering and nuclear magnetic resonance. The prevailing fitting algorithm in all these applications is based on alternating least squares (ALS) optimization, which is matched to Gaussian noise. In many cases, however, measurement errors are far from being Gaussian. In this paper, we develop an iterative algorithm for least absolute error fitting of general multilinear models, based on efficient interior point methods for Linear Programming (LP). We also benchmark its performance in Laplacian, Cauchy, and Gaussian noise environments, versus the respective CRBs and the commonly used ALS algorithm.
AB - PARAllel FACtor (PARAFAC) analysis is an extension of low-rank matrix decomposition to higher-way arrays. It decomposes a given array in a sum of multilinear terms. PARAFAC analysis generalizes and unifies common array processing models (like joint diagonalization and ESPRIT); it has found numerous applications from blind multiuser detection and multi-dimensional harmonic retrieval, to clustering and nuclear magnetic resonance. The prevailing fitting algorithm in all these applications is based on alternating least squares (ALS) optimization, which is matched to Gaussian noise. In many cases, however, measurement errors are far from being Gaussian. In this paper, we develop an iterative algorithm for least absolute error fitting of general multilinear models, based on efficient interior point methods for Linear Programming (LP). We also benchmark its performance in Laplacian, Cauchy, and Gaussian noise environments, versus the respective CRBs and the commonly used ALS algorithm.
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M3 - Conference article
AN - SCOPUS:4644285959
VL - 2
SP - II113-II116
JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
SN - 0736-7791
T2 - Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing
Y2 - 17 May 2004 through 21 May 2004
ER -