Dencentralized learning in the presence of low-rank noise

TL;DR

This paper introduces a distributed algorithm enabling networked agents to enhance observation reliability by projecting data onto a low-dimensional subspace, effectively handling low-rank noise in a decentralized manner.

Contribution

It develops a novel distributed oblique projection algorithm for low-rank noise mitigation, extending centralized solutions to networked, local computations.

Findings

The algorithm effectively reduces noise in distributed network observations.

It converges iteratively to the true low-rank signal subspace.

The method is applicable to adaptive learning over networks.

Abstract

Observations collected by agents in a network may be unreliable due to observation noise or interference. This paper proposes a distributed algorithm that allows each node to improve the reliability of its own observation by relying solely on local computations and interactions with immediate neighbors, assuming that the field (graph signal) monitored by the network lies in a low-dimensional subspace and that a low-rank noise is present in addition to the usual full-rank noise. While oblique projections can be used to project measurements onto a low-rank subspace along a direction that is oblique to the subspace, the resulting solution is not distributed. Starting from the centralized solution, we propose an algorithm that performs the oblique projection of the overall set of observations onto the signal subspace in an iterative and distributed manner. We then show how the oblique projection framework can be extended to handle distributed learning and adaptation problems over networks.