Spectral clustering uses the top m eigenvectors of the normalized Laplacian matrix to embed high-dimensional data in R^m. We analyze the geometric structure of an embedded dataset drawn from a non-parametric mixture of m distributions. We begin our analysis at the population level by approximating the span of square root kernelized mixture densities from the principal eigenspace of the Laplacian...