TBSSvis: Visual analytics for Temporal Blind Source Separation

Piccolotto, Nikolaus; Bögl, Markus; Gschwandtner, Theresia; Muehlmann, Christoph; Nordhausen, Klaus; Filzmoser, Peter; Miksch, Silvia

doi:10.1016/j.visinf.2022.10.002

Cited by 6 publications

(10 citation statements)

References 74 publications

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“…Second, TBSSvis applies a custom clustering scheme (Piccolotto et al 2022a) that respects the set structure of latent components, unlike popular clustering algorithms such as k-means. Specifically, the clustering scheme groups by component similarity (quantified by absolute Pearson correlation) but only groups components from distinct models into the same cluster (Figure 3b).…”

Section: Results Explorationmentioning

confidence: 99%

“…TBSSvis (Piccolotto et al 2022a) is an interactive VA software tool to support TBSS analysis in the context of gSOBI. It is available on GitHub (Piccolotto 2022), where detailed instructions on how to run TBSSvis are included. A video demo (using another dataset than that presented in Section 4) can be found on YouTube 1 .…”

Section: Tbssvismentioning

confidence: 99%

“…They found TBSSvis to be a time-saver and very useful for applied work. The VA aspects in the design of TBSSvis are discussed in detail in Piccolotto et al (2022a). We will present in this section only the most important views, visualizations, and concepts.…”

Section: Tbssvismentioning

confidence: 99%

“…In prior work (Piccolotto et al 2022a), the design and evaluation of such a VA solution for the temporal BSS method gSOBI (Miettinen et al 2020) were discussed. However, the paper focused on visualization designs and data mining algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Visual Interactive Parameter Selection for Temporal Blind Source Separation

Cappello,

Piccolotto,

Muehlmann

et al. 2024

J DAT SCI STAT VIS

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Many fields of science and industry collect and analyze multivariate time-varying measurements, e.g., healthcare, geophysics, or finance. Such data is often high-dimensional, correlated, and noisy. Experts are interested in latent components of the dataset, but due to the aforementioned properties these are difficult to obtain. Temporal Blind Source Separation (TBSS) is a suitable and well-established framework for these data. However, the large choice of methods and their tuning parameters impedes the effective use of TBSS in practice. The goal of Visual Analytics (VA) is to create powerful analytic tools by combining the strengths of humans and computers. We designed, developed, and evaluated VA contributions in previous work to support TBSS-related analysis tasks. In this paper, we highlight the benefits and opportunities of VA concepts for statistic-oriented problems using a real-world TBSS application example with a dataset of climate and meteorological measurements in Italy.

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Section: Results Explorationmentioning

confidence: 99%

Section: Tbssvismentioning

confidence: 99%

Section: Tbssvismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Visual Interactive Parameter Selection for Temporal Blind Source Separation

Cappello,

Piccolotto,

Muehlmann

et al. 2024

J DAT SCI STAT VIS

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“…Characterizing the large sample behaviour of local autocovariance matrices and the unmixing matrix estimators is desirable in order to build asymptotic tests and provide estimation errors. Furthermore, to aid practitioners in choosing reasonable kernel settings, visual analytic tools which were developed for SBSS, AMUSE and SOBI in Piccolotto et al (2022aPiccolotto et al ( , 2022b will be extended to the spatio-temporal case.…”

Section: Discussionmentioning

confidence: 99%

Blind recovery of sources for multivariate space-time random fields

Muehlmann

Iaco

Nordhausen

2022

Stoch Environ Res Risk Assess

Self Cite

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With advances in modern worlds technology, huge datasets that show dependencies in space as well as in time occur frequently in practice. As an example, several monitoring stations at different geographical locations track hourly concentration measurements of a number of air pollutants for several years. Such a dataset contains thousands of multivariate observations, thus, proper statistical analysis needs to account for dependencies in space and time between and among the different monitored variables. To simplify the consequent multivariate spatio-temporal statistical analysis it might be of interest to detect linear transformations of the original observations that result in straightforward interpretative, spatio-temporally uncorrelated processes that are also highly likely to have a real physical meaning. Blind source separation (BSS) represents a statistical methodology which has the aim to recover so-called latent processes, that exactly meet the former requirements. BSS was already successfully used in sole temporal and sole spatial applications with great success, but, it was not yet introduced for the spatio-temporal case. In this contribution, a reasonable and innovative generalization of BSS for multivariate space-time random fields (stBSS), under second-order stationarity, is proposed, together with two space-time extensions of the well-known algorithms for multiple unknown signals extraction (stAMUSE) and the second-order blind identification (stSOBI) which solve the formulated problem. Furthermore, symmetry and separability properties of the model are elaborated and connections to the space-time linear model of coregionalization and to the classical principal component analysis are drawn. Finally, the usefulness of the new methods is shown in a thorough simulation study and on a real environmental application.

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Order Determination in Second-Order Source Separation Models Using Data Augmentation

Radojičić,

Nordhausen

2024

Advances in Intelligent Systems and Computing

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TBSSvis: Visual analytics for Temporal Blind Source Separation

Cited by 6 publications

References 74 publications

Visual Interactive Parameter Selection for Temporal Blind Source Separation

Visual Interactive Parameter Selection for Temporal Blind Source Separation

Blind recovery of sources for multivariate space-time random fields

Order Determination in Second-Order Source Separation Models Using Data Augmentation

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