UC Davis

We propose a new algorithm for tensor decomposition, based on \algname~algorithm, and apply our new algorithmic ideas to blind deconvolution and Gaussian mixture models.Our first contribution is a simple and efficient algorithm to decompose certain symmetric overcomplete order-3 tensors, that is, three dimensional arrays of the form $\tensor T = \sum_{i=1}^n \vm a_i \otimes \vm a_i \otimes \vm a_i$ where the $\vm a_i$s are not linearly independent. Our algorithm comes with a detailed robustness analysis. Our second contribution builds on top of our tensor decomposition algorithm to expand the family of Gaussian mixture models whose parameters can be estimated efficiently. These ideas are also presented in a more general framework of blind deconvolution that makes them applicable to mixture models of identical but very general distributions, including all centrally symmetric distributions with finite 6th moment.