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Abstract:
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Dynamic tensor data are becoming prevalent in numerous Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor. Also there is often a gap between statistical guarantee and computational efficiency for existing tensor clustering solutions. In this talk, I will introduce a new dynamic tensor clustering method, which takes into account both sparsity and fusion structures, and enjoys strong statistical guarantees as well as high computational efficiency. The efficacy of our approach will be illustrated via dynamic functional connectivity analysis. This is a joint work with Lexin Li.
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