The problem of n-dimensional orthogonal linear regression is a problem of finding an n-dimensional hyperplane minimising the sum of Euclidean distances between this hyperplane and a given set of m points, where m≥n. This nonlinear programming problem has been re-cast in an augmented matrix form and solved as a sequence of iteratively re-weighted least square problems. The proposed algorithm is seen as an alternative to the recently published algorithm by Cavalier and Melloy.
