Reduced rank approximation of matrices has hitherto been possible only by unweighted least squares. This paper presents iterative techniques for obtaining such approximations when weights are ...
We consider polynomial approximation on the unit sphere S² = {(x, y, z) Є R³ : x² + y² + z² = 1} by a class of regularized discrete least squares methods with novel choices for the regularization ...
CATALOG DESCRIPTION: discrete-time random process, second-order statistics, autoregressive and moving average processes, linear prediction, Wiener filter, stochastic gradient (Least Mean Square) ...
In this paper we present an algorithm to enhance the accuracy of the estimation of the parameters of linear stroke segments in a two-dimensional printed character image. The algorithm achieves high ...
Penalized least squares estimates provide a way to balance fitting the data closely and avoiding excessive roughness or rapid variation. A penalized least squares estimate is a surface that minimizes ...
Most straight-line approximations for data points are found using the method of least squares. This method works for many applications, but it cannot guarantee that the greatest vertical distance ...
In this talk we introduce an approach that augments least-squares finite element formulations with user-specified goals or quantities-of-interest. The method incorporates the quantity-of-interest into ...