  In this short note we extend some of the recent results on matrix completion
under the assumption that the columns of the matrix can be grouped (clustered)
into subspaces (not necessarily disjoint or independent). This model deviates
from the typical assumption prevalent in the literature dealing with
compression and recovery for big-data applications. The results have a direct
bearing on the problem of subspace clustering under missing or incomplete
information.
