  Let us consider a case where all of the elements in some continuous slices
are missing in tensor data.
  In this case, the nuclear-norm and total variation regularization methods
usually fail to recover the missing elements.
  The key problem is capturing some delay/shift-invariant structure.
  In this study, we consider a low-rank model in an embedded space of a tensor.
  For this purpose, we extend a delay embedding for a time series to a
"multi-way delay-embedding transform" for a tensor, which takes a given
incomplete tensor as the input and outputs a higher-order incomplete Hankel
tensor.
  The higher-order tensor is then recovered by Tucker-based low-rank tensor
factorization.
  Finally, an estimated tensor can be obtained by using the inverse multi-way
delay embedding transform of the recovered higher-order tensor.
  Our experiments showed that the proposed method successfully recovered
missing slices for some color images and functional magnetic resonance images.
