An analysis of the total least squares problem
GH Golub, CF Van Loan - SIAM journal on numerical analysis, 1980 - SIAM
GH Golub, CF Van Loan
SIAM journal on numerical analysis, 1980•SIAMTotal Least Squares (TLS) is a method of fitting that is appropriate when there are errors in
both the observation vector b(m*1) and in the data matrix A(m*n). The technique has been
discussed by several authors, and amounts to fitting a “best” subspace to the points
(a_i^T,b_i),i=1,⋯,m, where a_i^T is the i th row of A. In this paper a singular value
decomposition analysis of the TLS problem is presented. The sensitivity of the TLS problem
as well as its relationship to ordinary least squares regression is explored. An algorithm for …
both the observation vector b(m*1) and in the data matrix A(m*n). The technique has been
discussed by several authors, and amounts to fitting a “best” subspace to the points
(a_i^T,b_i),i=1,⋯,m, where a_i^T is the i th row of A. In this paper a singular value
decomposition analysis of the TLS problem is presented. The sensitivity of the TLS problem
as well as its relationship to ordinary least squares regression is explored. An algorithm for …
Total Least Squares (TLS) is a method of fitting that is appropriate when there are errors in both the observation vector and in the data matrix . The technique has been discussed by several authors, and amounts to fitting a “best” subspace to the points , where is the ith row of A. In this paper a singular value decomposition analysis of the TLS problem is presented. The sensitivity of the TLS problem as well as its relationship to ordinary least squares regression is explored. An algorithm for solving the TLS problem is proposed that utilizes the singular value decomposition and which provides a measure of the underlying problem’s sensitivity.
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