English

Federated Over-Air Subspace Tracking from Incomplete and Corrupted Data

Machine Learning 2022-07-01 v4 Information Theory Numerical Analysis math.IT Numerical Analysis Machine Learning

Abstract

In this work we study the problem of Subspace Tracking with missing data (ST-miss) and outliers (Robust ST-miss). We propose a novel algorithm, and provide a guarantee for both these problems. Unlike past work on this topic, the current work does not impose the piecewise constant subspace change assumption. Additionally, the proposed algorithm is much simpler (uses fewer parameters) than our previous work. Secondly, we extend our approach and its analysis to provably solving these problems when the data is federated and when the over-air data communication modality is used for information exchange between the KK peer nodes and the center. We validate our theoretical claims with extensive numerical experiments.

Cite

@article{arxiv.2002.12873,
  title  = {Federated Over-Air Subspace Tracking from Incomplete and Corrupted Data},
  author = {Praneeth Narayanamurthy and Namrata Vaswani and Aditya Ramamoorthy},
  journal= {arXiv preprint arXiv:2002.12873},
  year   = {2022}
}

Comments

To appear in IEEE Transactions on Signal Processing. changes to writing; more general result provided from which previous result follows as special case

R2 v1 2026-06-23T13:58:00.590Z