  Low-rank matrix completion has been studied extensively under various type of
categories. The problem could be categorized as noisy completion or exact
completion, also active or passive completion algorithms. In this paper we
focus on adaptive matrix completion with bounded type of noise. We assume that
the matrix $\mathbf{M}$ we target to recover is composed as low-rank matrix
with addition of bounded small noise. The problem has been previously studied
by \cite{nina}, in a fixed sampling model. Here, we study this problem in
adaptive setting that, we continuously estimate an upper bound for the angle
with the underlying low-rank subspace and noise-added subspace. Moreover, the
method suggested here, could be shown requires much smaller observation than
aforementioned method.

Machine Learning