A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration

Cavallo, Marco; Demiralp, Çağatay

doi:10.1145/3173574.3174209

Cited by 31 publications

(18 citation statements)

References 52 publications

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“…Furthermore, we excluded the works which are not generalizable and focus on specific domain applications such as [19], [20]. [47] ✓ ✓ ✓ GEP [63] ✓ ✓ (✓) T+SC ccPCA [44] ✓ ✓ T+SC DimReader [45] ✓ (✓) SC Coimbra et al [42] ✓ ✓ ✓ ✓ T+SC Praxis [46] ✓ (✓) ✓ FocusChanger [50] ✓ ✓ Probing Proj. [36] ✓ ✓ T+SC ProxiLens [34] ✓ Stress Maps [29] ✓…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, ccPCA does not deal with the analysis of shapes, which we support with our proposed Dimension Correlation. Other recent approaches include DimReader [45], where the authors create so-called generalized axes for non-linear DR methods, but besides explaining a single dimension at a time, it is currently unclear how exactly it can be used in an interactive exploration scenario; and Praxis [46], with two methods-backward and forward projection-but it requires fast out-of-sample extensions which are not available for the original t-SNE.…”

Section: Interpretation Of Projectionsmentioning

confidence: 99%

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t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections

Chatzimparmpas

Martins

Kerren

2020

IEEE Trans. Visual. Comput. Graphics

127

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t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections can be hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this work, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool's effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.

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Section: Related Workmentioning

confidence: 99%

Section: Interpretation Of Projectionsmentioning

confidence: 99%

t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections

Chatzimparmpas

Martins

Kerren

2020

IEEE Trans. Visual. Comput. Graphics

127

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“…Since low-dimensional projections are generally lossy representations of high-dimensional data relations, researchers have introduced visual methods to convey and correct dimensionality reduction errors [7,20,45,71]. Similarly, enhanced biplots [22,34] and prolines [16,30] have been introduced to visualize the contribution of data features to the DR plane. Researchers have also used direct manipulation to interactively modify data through DR visualizations [38,60] and out-of-sample extrapolation [11,75].…”

Section: Tools For Exploratory Data Analysismentioning

confidence: 99%

“…While a very powerful means to identify structures and outliers in the data, scatterplots of dimensionally reduced data generally lack interpretability as to the contribution of specific data features to the projection. To mitigate this, we complement the scatterplot visualization with prolines [16], a generalized version of biplot [34] that introduces axes representative of the original data dimensions. Each proline axis indicates the relevance and directionality of increase for a feature, and provides statistical information about that featureâĂŹs distribution.…”

Section: Data Viewsmentioning

confidence: 99%

“…The ability to visualize an extra dimension in the dimensionality reduction, and the larger surface (volume) available for the projection, together represent an opportunity to improve the interpretability of projection axes, reduce visual clutter, and introduce direct manipulation of data samples. To support sensitivity and what-if analysis, we have introduced a tridimensional generalization of the forward and backward projection interactions originally proposed by Cavallo in [16]. Through forward projection, the user can specify through voice (or manually from the Data table view) a perturbation in the feature values of a selected data sample (e.g.…”

Section: Augmented Reality Integrationmentioning

confidence: 99%

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Immersive Insights: A Hybrid Analytics System forCollaborative Exploratory Data Analysis

Cavallo¹,

Dholakia²,

Havlena³

et al. 2019

25th ACM Symposium on Virtual Reality Software and Technology

Self Cite

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Figure 1: Immersive Insights is a collaborative, hybrid analytics system for exploratory data analysis. The application, implemented in a Dataspace environment [18], leverages multimodal interaction and combines the use of technologies such as 1) high-resolution movable screens to visualize statistical information, 2) a central projection table providing an overview of the current analysis, and 3) an augmented reality view to visualize and interact with high-dimensional data. ABSTRACTIn the past few years, augmented reality (AR) and virtual reality (VR) technologies have experienced terrific improvements in both accessibility and hardware capabilities, encouraging the application of these devices across various domains. While researchers have demonstrated the possible advantages of AR and VR for certain data science tasks, it is still unclear how these technologies would perform in the context of exploratory data analysis (EDA) at large. In particular, we believe it is important to better understand which level of immersion EDA would concretely benefit from, and to quantify the contribution of AR and VR with respect to standard analysis workflows.In this work, we leverage a Dataspace reconfigurable hybrid reality environment to study how data scientists might perform EDA in a co-located, collaborative context. Specifically, we propose the design and implementation of Immersive Insights, a hybrid analytics system combining high-resolution displays, table projections, and augmented reality (AR) visualizations of the data.We conducted a two-part user study with twelve data scientists, in which we evaluated how different levels of data immersion affect the EDA process and compared the performance of Immersive Insights with a state-of-the-art, non-immersive data analysis system.

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Explainable Interactive Projections for Image Data

Han

Faust

Norambuena

et al. 2022

Advances in Visual Computing

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Making sense of large collections of images is difficult. Dimension reductions (DR) assist by organizing images in a 2D space based on similarities, but provide little support for explaining why images were placed together or apart in the 2D space. Additionally, they do not provide support for modifying and updating the 2D space to explore new relationships and organizations of images. To address these problems, we present an interactive DR method for images that uses visual features extracted by a deep neural network to project the images into 2D space and provides visual explanations of image features that contributed to the 2D location. In addition, it allows people to directly manipulate the 2D projection space to define alternative relationships and explore subsequent projections of the images. With an iterative cycle of semantic interaction and explainable-AI feedback, people can explore complex visual relationships in image data. Our approach to human-AI interaction integrates visual knowledge from both human mental models and pre-trained deep neural models to explore image data. Two usage scenarios are provided to demonstrate that our method is able to capture human feedback and incorporate it into the model. Our visual explanations help bridge the gap between the feature space and the original images to illustrate the knowledge learned by the model, creating a synergy between human and machine that facilitates a more complete analysis experience. Firstly, I would like to thank my advisor Dr. Christopher North for his continued support in my research study. He was and remains my best role model for a teacher, mentor, and researcher.

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A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration

Cited by 31 publications

References 52 publications

t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections

t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections

Immersive Insights: A Hybrid Analytics System forCollaborative Exploratory Data Analysis

Explainable Interactive Projections for Image Data

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