In this article, we consider a particular data structure consisting of a union of several nested low-rank subspaces with missing data entries. Given the rank of each subspace, we treat the data completion problem, i.e., to estimate the missing entries. Starting from the case of two-dimensional data, i.e., matrices, we show that the union of nested subspaces data structure leads to a structured decomposition U = XY where the factor Y has blocks of zeros that are determined by the rank values. Moreover, for high-dimensional data, i.e., tensors, we show that a similar structured CP decomposition also exists, U = Σl=1r a1l ⊗ a2l ⊗ ··· ⊗ adl, where Ad = [ad1 ···adr] contains blocks of zeros determined by the rank values. Based on such structured decompositions, we develop efficient alternating minimization algorithms for both matrix and tensor completions, by enforcing the above structures in each iteration including the initialization. Compared with naive approaches where either the additional rank constraints are ignored, or data completion is performed part by part, the proposed structured alternating minimization approaches exhibit faster convergence and higher recovery accuracy.
