TBSSvis: Visual analytics for Temporal Blind Source Separation
نویسندگان
چکیده
Temporal Blind Source Separation (TBSS) is used to obtain the true underlying processes from noisy temporal multivariate data, such as electrocardiograms. TBSS has similarities Principal Component Analysis (PCA) it separates input data into univariate components and applicable suitable datasets various domains, medicine, finance, or civil engineering. Despite TBSS’s broad applicability, involved tasks are not well supported in current tools, which offer only text-based interactions single static images. Analysts limited analyzing comparing obtained results, consist of diverse matrices sets time series. Additionally, parameter settings have a big impact on separation performance, but consequence improper tooling, analysts currently do consider whole space. We propose solve these problems by applying visual analytics (VA) principles. Our primary contribution design study for TBSS, so far been explored visualization community. developed task abstraction user-centered process. Task-specific assembling well-established techniques algorithms gain insights our secondary contribution. present TBSSvis, an interactive web-based VA prototype, we evaluated extensively two interviews with five experts. Feedback observations show that TBSSvis supports actual workflow combination visualizations facilitate results.
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ژورنال
عنوان ژورنال: Visual Informatics
سال: 2022
ISSN: ['2468-502X', '2543-2656']
DOI: https://doi.org/10.1016/j.visinf.2022.10.002