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Total Least Squares: State-of-the-Art Regression in Numerical Analysis
Vol. 36, No. 2 (Jun., 1994), pp. 258-264
Published by: Society for Industrial and Applied Mathematics
Stable URL: http://www.jstor.org/stable/2132463
Page Count: 7
You can always find the topics here!Topics: Least squares, Eigenvalues, Data lines, Hyperplanes, Eigenvectors, Numerical analysis, Geometric planes, Normal vectors, Applied mathematics, College mathematics
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Total least squares regression (TLS) fits a line to data where errors may occur in both the dependent and independent variables. In higher dimensions, TLS fits a hyperplane to such data. The elementary algorithm presented here fits readily in a first course in numerical linear algebra.
SIAM Review © 1994 Society for Industrial and Applied Mathematics