Efficient computational schemes for the orthogonal least squares algorithm

The orthogonal least squares (OM) algorithm is an efficient implementation of the forward selection method for subset model selection. The ability to find good subset parameters with only a linearly increasing computational requirement makes this method attractive lor practical implementations. In this correspondence, we examine the computational complexity of the algorithm and present a preprocessing inethod for reducing the computational requirement.