Robust On-line Matrix Completion on Graphs

TL;DR

This paper introduces algorithms for online robust matrix completion that leverage graph structures to accurately reconstruct incomplete data vectors in real-time, even with outliers, demonstrating superior performance over existing methods.

Contribution

The paper proposes novel graph-based algorithms for online robust matrix completion, with theoretical analysis and validation on synthetic and real datasets.

Findings

Algorithms outperform state-of-the-art methods

Effective in handling outliers and missing data

Validated on real-world datasets

Abstract

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.