Low Rank Tensor Methods in High Dimensional Data Analysis Part 2

Ming Yuan
Columbia University

Large amount of multidimensional data in the form of multilinear arrays, or tensors, arise routinely in modern applications from such diverse fields as chemometrics, genomics, physics, psychology, and signal processing among many others. At the moment, our ability to generate and acquire them has far outpaced our ability to effectively extract useful information from them. There is a clear demand to develop novel statistical methods, efficient computational algorithms, and fundamental mathematical theory to analyze and exploit information in these types of data. In this talk, I will review some of the recent progresses and discuss some of the present challenges.

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