A spectral learning framework for graphical models
I will present an extended version of the spectral algorithm which can be applied to graphs. This algorithm can be used as a learning algorithm for graphical models - directed and undirected. It can be used in a density estimation task on a distribution of labelled graphs. This algorithm is proven to converge, and is not prone to local extrema.