An important problem in applications is the approximation of a function f
from a finite set of randomly scattered data f(xj). A common and powerful
approach is to construct a trigonometric least squares approximation based on
the set of exponentials {e2πikx}. This leads to fast numerical
algorithms, but suffers from disturbing boundary effects due to the underlying
periodicity assumption on the data, an assumption that is rarely satisfied in
practice. To overcome this drawback we impose Neumann boundary conditions on
the data. This implies the use of cosine polynomials cos(πkx) as basis
functions. We show that scattered data approximation using cosine polynomials
leads to a least squares problem involving certain Toeplitz+Hankel matrices. We
derive estimates on the condition number of these matrices. Unlike other
Toeplitz+Hankel matrices, the Toeplitz+Hankel matrices arising in our context
cannot be diagonalized by the discrete cosine transform, but they still allow a
fast matrix-vector multiplication via DCT which gives rise to fast conjugate
gradient type algorithms. We show how the results can be generalized to higher
dimensions. Finally we demonstrate the performance of the proposed method by
applying it to a two-dimensional geophysical scattered data problem