Streaming Algorithms for Subspace Analysis: Comparative Review and Implementation on IoT Devices

Alex Marchioni, Luciano Prono, Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti

Research output: Contribution to journalArticlepeer-review


Subspace analysis (SA) is a widely used technique for coping with high-dimensional data and is becoming a fundamental step in the early treatment of many signal-processing tasks. However, traditional SA often requires a large amount of memory and computational resources, as it is equivalent to eigenspace determination. To address this issue, specialized streaming algorithms have been developed, allowing SA to be run on low-power devices, such as sensors or edge devices. Here, we present a classification and a comparison of these methods by providing a consistent description and highlighting their features and similarities. We also evaluate their performance in the task of subspace identification with a focus on computational complexity and memory footprint for different signal dimensions. Additionally, we test the implementation of these algorithms on common hardware platforms typically employed for sensors and edge devices.
Original languageEnglish (US)
Pages (from-to)12798-12810
Number of pages13
JournalIEEE Internet of Things Journal
Issue number14
StatePublished - Mar 14 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-09-05
Acknowledgements: This work was supported in part by PNRR— M4C2—Investimento 1.3, Partenariato Esteso PE00000013—“FAIR—Future Artificial Intelligence Research”—Spoke 8 “Pervasive AI,” funded by the European Commission under the NextGeneration EU Programme.

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Information Systems and Management
  • Computer Science Applications
  • Hardware and Architecture
  • Computer Networks and Communications


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