Smoothed Analysis of Tensor Decompositions

  title={Smoothed Analysis of Tensor Decompositions},
  author={Aditya Bhaskara and Moses Charikar and Ankur Moitra and Aravindan Vijayaraghavan},
Low rank decomposition of tensors is a powerful tool for learning generative models. The uniqueness results that hold for tensors give them a significant advantage over matrices. However, tensors pose serious algorithmic challenges; in particular, much of the matrix algebra toolkit fails to generalize to tensors. Efficient decomposition in the overcomplete case (where rank exceeds dimension) is particularly challenging. We introduce a smoothed analysis model for studying these questions and… CONTINUE READING
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