1 research outputs found
Robust On-line Matrix Completion on Graphs
We study online robust matrix completion on graphs. At each iteration a
vector with some entries missing is revealed and our goal is to reconstruct it
by identifying the underlying low-dimensional subspace from which the vectors
are drawn. We assume there is an underlying graph structure to the data, that
is, the components of each vector correspond to nodes of a certain (known)
graph, and their values are related accordingly. We give algorithms that
exploit the graph to reconstruct the incomplete data, even in the presence of
outlier noise. The theoretical properties of the algorithms are studied and
numerical experiments using both synthetic and real world datasets verify the
improved performance of the proposed technique compared to other state of the
art algorithms