Total least squares

Known as: Least products regression, Standardised major axis regression, Reduced major axis regression 
In applied statistics, total least squares is a type of errors-in-variables regression, a least squares data modeling technique in which… (More)
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Highly Cited
2011
Highly Cited
2011
Solving linear regression problems based on the total least-squares (TLS) criterion has well-documented merits in various… (More)
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Highly Cited
2006
Highly Cited
2006
In this paper, we present a method for removing noise from digital images corrupted with additive, multiplicative, and mixed… (More)
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Highly Cited
2002
Highly Cited
2002
The total least squares method is a numerical linear algebra tool for finding approximate solutions to overdetermined systems of… (More)
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2002
2002
The standard approaches to solving overdetermined linear systems Ax ≈ b construct minimal corrections to the vector b and/or the… (More)
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Highly Cited
1999
Highly Cited
1999
Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditioned coefficient matrix, and in… (More)
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Highly Cited
1997
Highly Cited
1997
The total least squares (TLS) method is a successful method for noise reduction in linear least squares problems in a number of… (More)
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Highly Cited
1995
Highly Cited
1995
In this paper, the problem of restoring an image distorted by a linear space-invariant (LSI) point-spread function (PSF) that is… (More)
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Highly Cited
1994
Highly Cited
1994
Structured rank-deficient matrices arise in many applications in signal processing, system identification, and control theory. We… (More)
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Highly Cited
1987
Highly Cited
1987
The resolution of the estimated closely spaced frequencies of the multiple sinusoids degrades as the signal-to-noise ratio (SNR… (More)
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Highly Cited
1984
Highly Cited
1984
Classical least squares regression consists of minimizing the sum of the squared residuals. Many authors have produced more… (More)
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