  Exact matrix completion and low rank matrix estimation problems has been
studied in different underlying conditions. In this work we study exact
low-rank completion under non-degenerate noise model. Non-degenerate random
noise model has been previously studied by many researchers under given
condition that the noise is sparse and existing in some of the columns. In this
paper, we assume that each row can receive random noise instead of columns and
propose an interactive algorithm that is robust to this noise. We show that we
use a parametrization technique to give a condition when the underlying matrix
could be recoverable and suggest an algorithm which recovers the underlying
matrix.
