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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Evaluation of visualization algorithms for CommSense system


    Contributors:


    Publication date :

    2022-06-01


    Size :

    724483 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    LTE commsense for object detection in indoor environments

    Sardar, Santu / Mishra, Amit K. / Khan, M. Z. A. | IEEE | 2018




    Metric visualization system for model evaluation

    BESSON CLEMENT / PURDY SCOTT M / RAGHAVAN BHARADWAJ | European Patent Office | 2024

    Free access

    METRIC VISUALIZATION SYSTEM FOR MODEL EVALUATION

    BESSON CLEMENT / PURDY SCOTT M / RAGHAVAN BHARADWAJ | European Patent Office | 2025

    Free access