Tucker generalizes PCA

PCA compresses a matrix — two axes, and it can never see more. Tucker generalizes it to a tensor of any order: one factor matrix per axis, plus a core tensor describing how they combine.

SVD compresses one matrix view; Tucker uses one compressed basis for each tensor mode.

That single sentence is the whole section. Everything else is how to compute it, and what it costs.