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

Subspace analysis 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 subspace analysis often requires a large amount of memory and computational resources, as it is equivalen...

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Bibliographic Details
Published in:IEEE internet of things journal 2023-07, Vol.10 (14), p.1-1
Main Authors: Marchioni, Alex, Prono, Luciano, Mangia, Mauro, Pareschi, Fabio, Rovatti, Riccardo, Setti, Gianluca
Format: Article
Language:English
Subjects:
Algorithms
Edge computing
Internet of Things
Minor Component Analysis
Minor Subspace
Principal Components Analysis
Principal Subspace
Sensors
Signal processing
Streaming Algorithms
Subspaces
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Summary:Subspace analysis 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 subspace analysis 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 subspace analysis 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.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3256529