Matrix Estimation with Adjustable Sampling Rate
Here’s a very interesting matrix completion method and result, which is a direct corollary from Koltchinskii, Lounici and Tsybakov (2011) paper. Consider the following matrix estimation problem: for input matrix $A\in \mathbb R^{m_1\times m_2}$, assume $m_1 < m_2$ and $\text{rank}(A) = r \ll m_2$. $n$ entries of $A$ are observed at uniformly at random, with independent noise $\epsilon_{ij}$. Denote as $\Omega$ the index set of observed entries of $A$, define the scaled observation matrix $Y = [y_{ij}]$ as ...
