For a probability measure on a real separable Hilbert space, we are
interested in "volume-based" approximations of the d-dimensional least squares
error of it, i.e., least squares error with respect to a best fit d-dimensional
affine subspace. Such approximations are given by averaging real-valued
multivariate functions which are typically scalings of squared (d+1)-volumes of
(d+1)-simplices. Specifically, we show that such averages are comparable to the
square of the d-dimensional least squares error of that measure, where the
comparison depends on a simple quantitative geometric property of it. This
result is a higher dimensional generalization of the elementary fact that the
double integral of the squared distances between points is proportional to the
variance of measure. We relate our work to two recent algorithms, one for
clustering affine subspaces and the other for Monte-Carlo SVD based on volume
sampling
