Comparing and Exploring High-Dimensional Data with Dimensionality Reduction Algorithms and Matrix Visualizations

Cutura, Rene; Aupetit, Michaël; Fekete, Jean-Daniel; Sedlmair, Michael

doi:10.1145/3399715.3399875

Cited by 20 publications

(19 citation statements)

References 41 publications

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“…Xia et al [11] introduce a technique for finding clusters and outliers in embeddings based on successive projections that maximize a marked pattern's saliency. Other tools focus on the interactive comparison of embeddings, based on dissimilarity matrices [12], linking clusters between embeddings [13], or visualizations of neighborhood overlap [14].…”

Section: Exploration Of Embedding Spacesmentioning

confidence: 99%

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Eckelt

Hinterreiter

Adelberger

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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In this work, we propose an interactive visual approach for the exploration and formation of structural relationships in embeddings of high-dimensional data. These structural relationships, such as item sequences, associations of items with groups, and hierarchies between groups of items, are defining properties of many real-world datasets. Nevertheless, most existing methods for the visual exploration of embeddings treat these structures as second-class citizens or do not take them into account at all. In our proposed analysis workflow, users explore enriched scatterplots of the embedding, in which relationships between items and/or groups are visually highlighted. The original high-dimensional data for single items, groups of items, or differences between connected items and groups is accessible through additional summary visualizations. We carefully tailored these summary and difference visualizations to the various data types and semantic contexts. During their exploratory analysis, users can externalize their insights by setting up additional groups and relationships between items and/or groups. We demonstrate the utility and potential impact of our approach by means of two use cases and multiple examples from various domains.

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Section: Exploration Of Embedding Spacesmentioning

confidence: 99%

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Eckelt

Hinterreiter

Adelberger

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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“…The Latent Space Cartography technique by Liu et al [30] lets users explore embeddings of an autoencoder's latent space in multiple coordinated views with enriched scatterplots. Other tools focus on the interactive comparison of embeddings, based on dissimilarity matrices [6] or visualizations of neighborhood overlap [15]. Ma and Maciejewski [31] describe the analysis of class separations in embeddings through locally linear segments, which connects the work to other recent efforts to explain non-linear embeddings [2,11,23].…”

Section: Exploration Of Embedding Spacesmentioning

confidence: 99%

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Eckelt¹,

Hinterreiter²,

Adelberger³

et al. 2021

Preprint

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In this work, we propose an interactive visual approach for the exploration of structural relationships in embeddings of high-dimensional data. These structural relationships, such as item sequences, associations of items with groups, and hierarchies between groups of items, are defining properties of many real-world datasets. Nevertheless, most existing methods for the visual exploration of embeddings treat these structures as second-class citizens or do not take them into account at all. In our proposed analysis workflow, users explore enriched scatterplots of the embedding, in which relationships between items and/or groups are visually highlighted. The original high-dimensional data for single items, groups of items, or differences between connected items and groups is accessible through additional summary visualizations. We carefully tailored these summary and difference visualizations to the various data types and semantic contexts. During their exploratory analysis, users can externalize their insights by setting up additional groups and relationships between items and/or groups, thereby creating graphs that represent visual data stories. We demonstrate the utility and potential impact of our approach by means of two use cases and multiple examples from various domains.

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“…Plüger et al [32] developed an approach called VeCHart that detects similar stroke-patterns in prints and matches them in order to allow visual alignment and automated deviation highlighting for comparison purposes. Cutura et al [33] proposed a visual analysis approach called Compadre for comparing the distances of high-dimensional data and their low-dimensional projections. The key to visual analysis is a matrix visualization for representing the discrepancy between distance matrices, which are linked with 2D scatter plot projections of the data.…”

Section: Comparison Visualisationmentioning

confidence: 99%

Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation

2021

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Building an effective Machine Learning (ML) model for a data set is a difficult task involving various steps. One of the most important steps is to compare a substantial amount of generated ML models to find the optimal one for deployment. It is challenging to compare such models with a dynamic number of features. Comparison is more than only finding differences of ML model performance, as users are also interested in the relations between features and model performance such as feature importance for ML explanations. This paper proposes RadialNet Chart, a novel visualisation approach, to compare ML models trained with a different number of features of a given data set while revealing implicit dependent relations. In RadialNet Chart, ML models and features are represented by lines and arcs, respectively. These lines are generated effectively using a recursive function. The dependence of ML models with a dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections. ML model performance information is encoded with colour and line width in RadialNet Chart. Taken together with the structure of visualisation, feature importance can be directly discerned in RadialNet Chart for ML explanations. Compared with other commonly used visualisation approaches, RadialNet Chart can help to simplify the ML model comparison process with different benefits such as the following: more efficient in terms of helping users to focus their attention to find visual elements of interest and easier to compare ML performance to find optimal ML model and discern important features visually and directly instead of through complex algorithmic calculations for ML explanations.

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Comparing and Exploring High-Dimensional Data with Dimensionality Reduction Algorithms and Matrix Visualizations

Cited by 20 publications

References 41 publications

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Visual Exploration of Relationships and Structure in Low-Dimensional Embeddings

Facilitating Machine Learning Model Comparison and Explanation through a Radial Visualisation

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