In this talk I will describe a spectral framework for estimating Gaussian mixture distributions and clustering based on eigenfunctions of certain distribution-dependent convolution operators. By analyzing these eigenfunctions, our approach allows
us to provide estimates for various parameters, including the number of mixture components. I will also discuss connection to
Kernel Principal Components Analysis and Spectral clustering, and show some promising experimental results.