Evaluation of visualization algorithms for CommSense system

Sandip Jana, Amit Kumar Mishra, Mohammed Zafar Ali Khan

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

Application specific instrumentation (ASIN) makes use of sensors and AI (SensAI) algorithms for a highly specialized application, using less computational overhead, it can give good performance. This work evaluates the performance of communication based sensing (CommSense) system using Principal Component Analysis (PCA), kernel PCA (KPCA), t-distributed Stochastic Neighbour Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) algorithms and their quality of projection. In this paper, we have used Earth Mover's Distance (EMD) (also known as 1st Wasserstein Distance (WD)) for assessing the projections and we reach at the conclusion that, in terms of implementation PCA is the best, but for visualization KPCA, t-SNE and UMAP perform better than PCA.

Original languageEnglish
Title of host publication2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings
PublisherIEEE Press
Number of pages5
ISBN (Electronic)9781665482431
DOIs
Publication statusPublished - 25 Aug 2022
Externally publishedYes
Event95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring - Helsinki, Finland
Duration: 19 Jun 202222 Jun 2022

Publication series

NameIEEE Vehicular Technology Conference
Volume2022-June
ISSN (Print)1550-2252

Conference

Conference95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring
Country/TerritoryFinland
CityHelsinki
Period19 Jun 202222 Jun 2022

Keywords

  • ASIN
  • CommSense
  • Earth Mover's Distance
  • KPCA
  • PCA
  • t-SNE
  • UMAP
  • Wasserstein Distance

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