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.

Alex Marchioni, Luciano Prono, Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti (2023). Streaming Algorithms for Subspace Analysis: an Edge-and Hardware-oriented review. IEEE INTERNET OF THINGS JOURNAL, 10(14), 12798-12810 [10.1109/JIOT.2023.3256529].

Streaming Algorithms for Subspace Analysis: an Edge-and Hardware-oriented review

Alex Marchioni
;
Mauro Mangia;Riccardo Rovatti;
2023

Abstract

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.
2023
Alex Marchioni, Luciano Prono, Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti (2023). Streaming Algorithms for Subspace Analysis: an Edge-and Hardware-oriented review. IEEE INTERNET OF THINGS JOURNAL, 10(14), 12798-12810 [10.1109/JIOT.2023.3256529].
Alex Marchioni; Luciano Prono; Mauro Mangia; Fabio Pareschi; Riccardo Rovatti; Gianluca Setti
File in questo prodotto:
File Dimensione Formato  
STREAMING ALGHORITHMS FOR SUBSPACE ANALYSIS post print.pdf

accesso aperto

Tipo: Postprint
Licenza: Licenza per accesso libero gratuito
Dimensione 1.33 MB
Formato Adobe PDF
1.33 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/964668
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact