Tensor rank is a fundamental concept in multilinear algebra that refers to the minimum number of simple tensors needed to represent a given tensor as a sum. This concept is crucial when discussing decompositions like Tucker and CP, as it helps determine how tensors can be expressed and approximated in terms of their underlying structure. Understanding tensor rank provides insights into the complexity of the data represented by tensors and informs the choice of decomposition methods.
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