Dencentralized learning in the presence of low-rank noise
This work addresses the challenge of noise interference in decentralized sensor networks, offering a distributed solution for improved data reliability, though it is incremental as it builds on existing oblique projection methods.
The paper tackles the problem of unreliable observations in decentralized networks by proposing a distributed algorithm that improves observation reliability through local computations and neighbor interactions, assuming the monitored graph signal lies in a low-dimensional subspace with low-rank noise, and demonstrates its application to distributed learning and adaptation.
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.