47 research outputs found
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion
Low-rank matrix completion (LRMC) problems arise in a wide variety of
applications. Previous theory mainly provides conditions for completion under
missing-at-random samplings. This paper studies deterministic conditions for
completion. An incomplete matrix is finitely rank- completable
if there are at most finitely many rank- matrices that agree with all its
observed entries. Finite completability is the tipping point in LRMC, as a few
additional samples of a finitely completable matrix guarantee its unique
completability. The main contribution of this paper is a deterministic sampling
condition for finite completability. We use this to also derive deterministic
sampling conditions for unique completability that can be efficiently verified.
We also show that under uniform random sampling schemes, these conditions are
satisfied with high probability if entries per column are
observed. These findings have several implications on LRMC regarding lower
bounds, sample and computational complexity, the role of coherence, adaptive
settings and the validation of any completion algorithm. We complement our
theoretical results with experiments that support our findings and motivate
future analysis of uncharted sampling regimes.Comment: This update corrects an error in version 2 of this paper, where we
erroneously assumed that columns with more than r+1 observed entries would
yield multiple independent constraint
The algebraic combinatorial approach for low-rank matrix completion
We propose an algebraic combinatorial framework for the problem of completing
partially observed low-rank matrices. We show that the intrinsic properties of
the problem, including which entries can be reconstructed, and the degrees of freedom
in the reconstruction, do not depend on the values of the observed entries, but
only on their position. We associate combinatorial and algebraic objects, differentials
and matroids, which are descriptors of the particular reconstruction task, to the
set of observed entries, and apply them to obtain reconstruction bounds. We show
how similar techniques can be used to obtain reconstruction bounds on general compressed
sensing problems with algebraic compression constraints. Using the new
theory, we develop several algorithms for low-rank matrix completion, which allow
to determine which set of entries can be potentially reconstructed and which not,
and how, and we present algorithms which apply algebraic combinatorial methods
in order to reconstruct the missing entries
Matrix Completion for the Independence Model
We investigate the problem of completing partial matrices to rank-one matrices in the standard simplex ∆mn−1. The motivation for studying this problem comes from statistics: A lack of eligible completion can provide a falsification test for partial observations to come from the independence model. For each pattern of specified entries, we give equations and inequalities which are satisfied if and only if an eligible completion exists. We also describe the set of valid completions, and we optimize over this set