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- W2063552783 abstract "Numerically stable methods for the computations required in the expectation-maximization (EM) algorithm for maximum-likelihood structured covariance estimation are presented. It is shown that the basic computational task at each iteration is the calculation of a pseudoinverse of a certain linear system of equations. In the no-noise case this is a hard-decision, or Moore–Penrose, pseudoinverse, whereas in the additive noise case it is a soft-decision pseudoinverse. An approach to computing the soft-decision pseudoinverse, which can handle all combinations of dimension and rank in this system of equations, based on the singular value decomposition (SVD), is proposed. An alternative method based on the LQ factorization, which is applicable in certain circumstances, is also proposed. The intermediate calculations required in the EM algorithm can be used to calculate the log-likelihood and the gradient of the log-likelihood." @default.
- W2063552783 created "2016-06-24" @default.
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- W2063552783 date "2007-01-01" @default.
- W2063552783 modified "2023-09-25" @default.
- W2063552783 title "Numerically Stable Implementations of the Structured Covariance Expectation-Maximization Algorithm" @default.
- W2063552783 doi "https://doi.org/10.1137/040609495" @default.
- W2063552783 hasPublicationYear "2007" @default.
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