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An Analysis of the Total Least Squares Problem
Gene H. Golub and Charles F. Van Loan
SIAM Journal on Numerical Analysis
Vol. 17, No. 6 (Dec., 1980), pp. 883-893
Published by: Society for Industrial and Applied Mathematics
Stable URL: http://www.jstor.org/stable/2156807
Page Count: 11
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Total 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 (aT i,bi), i = 1, ⋯, m, where aT i 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.
SIAM Journal on Numerical Analysis © 1980 Society for Industrial and Applied Mathematics