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Activity Number: 87 - Reduced-Rank Methods: Seventy Years of History and State-of-the-Art Developments
Type: Invited
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: General Methodology
Abstract #320615
Title: Spectral Learning for High-Dimensional Tensors
Author(s): Ming Yuan*
Companies: Columbia University
Keywords: perturbation analysis; singular value decomposition; tensor
Abstract:

Many problems can be formulated as recovering a spectral learning of tensors. Although an increasingly common task, it remains a challenging problem because of the lack of tools for perturbation analysis similar to those for matrices. In this talk, I will describe some recent results towards a general treatment of these problems.


Authors who are presenting talks have a * after their name.

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