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Multivariate Volume Visualization through Dynamic Projections
"... temperature pressure Subspace View Navigation Panel Figure 1: An example of the semiautomatic transfer function (TF) design for Hurricane Isabel dataset. Left: subspace view navigation panel. Here each node represents a subspace view, views from the same subspace are grouped together with the same ..."
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temperature pressure Subspace View Navigation Panel Figure 1: An example of the semiautomatic transfer function (TF) design for Hurricane Isabel dataset. Left: subspace view navigation panel. Here each node represents a subspace view, views from the same subspace are grouped together with the same color, and arrows connecting the nodes indicate the current exploration path. Right: during the TF design process, we exploit relations among different parts of the data by exploring various subspace views via the navigation panel, and further modify and refine the TF. (a)(b) We import four subspace labels (into the PCA view in (a)) based on subspace clustering as the initial TF design, where parts of the hurricane eye are readily visible in the volume visualization (b). (ce) We explore the relations among different parts of the attribute space through dynamic projections between multiple subspace views. (fi) We further modify and refine the TF based on information we learn from the dynamic projections to arrive at the final visualization, where a cutaway view of the volume is shown in (i) that highlights different regions of the hurricane eye. To study the attributes that distinguish these regions, temperature and pressure profiles of the subspace view in (h) are shown in (j) and (k) respectively. See Section 5 for more details. We propose a multivariate volume visualization framework that tightly couples dynamic projections with a highdimensional trans
Interactive Image Feature Selection Aided by Dimensionality Reduction
"... Feature selection is an important step in designing image classification systems. While many automatic feature selection methods exist, most of them are opaque to their users. We consider that users should be able to gain insight into how observations behave in the feature space, since this may all ..."
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Feature selection is an important step in designing image classification systems. While many automatic feature selection methods exist, most of them are opaque to their users. We consider that users should be able to gain insight into how observations behave in the feature space, since this may allow the design of better features and the incorporation of domain knowledge. For this purpose, we propose a methodology for interactive and iterative selection of image features aided by dimensionality reduction plots and complementary exploration tools. We evaluate our proposal on the problem of feature selection for skin lesion image classification.
Visualizing HighDimensional Data: Advances in the Past Decade
"... Massive simulations and arrays of sensing devices, in combination with increasing computing resources, have generated large, complex, highdimensional datasets used to study phenomena across numerous fields of study. Visualization plays an important role in exploring such datasets. We provide a comp ..."
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Massive simulations and arrays of sensing devices, in combination with increasing computing resources, have generated large, complex, highdimensional datasets used to study phenomena across numerous fields of study. Visualization plays an important role in exploring such datasets. We provide a comprehensive survey of advances in highdimensional data visualization over the past 15 years. We aim at providing actionable guidance for data practitioners to navigate through a modular view of the recent advances, allowing the creation of new visualizations along the enriched information visualization pipeline and identifying future opportunities for visualization research.
1Visual Exploration of HighDimensional Data: Subspace Analysis through Dynamic Projections
, 2014
"... Understanding highdimensional data is rapidly becoming a central challenge in many areas of ..."
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Understanding highdimensional data is rapidly becoming a central challenge in many areas of
Visual Exploration of HighDimensional Data through Subspace Analysis and Dynamic Projections
"... We introduce a novel interactive framework for visualizing and exploring highdimensional datasets based on subspace analysis and dynamic projections. We assume the highdimensional dataset can be represented by a mixture of lowdimensional linear subspaces with mixed dimensions, and provide a metho ..."
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We introduce a novel interactive framework for visualizing and exploring highdimensional datasets based on subspace analysis and dynamic projections. We assume the highdimensional dataset can be represented by a mixture of lowdimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use realworld examples to demonstrate the novelty and usability of our proposed framework.
DistortionGuided StructureDriven Interactive Exploration of HighDimensional Data
"... Dimension reduction techniques are essential for feature selection and feature extraction of complex highdimensional data. These techniques, which construct lowdimensional representations of data, are typically geometrically motivated, computationally efficient and approximately preserve certain ..."
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Dimension reduction techniques are essential for feature selection and feature extraction of complex highdimensional data. These techniques, which construct lowdimensional representations of data, are typically geometrically motivated, computationally efficient and approximately preserve certain structural properties of the data. However, they are often used as black box solutions in data exploration and their results can be difficult to interpret. To assess the quality of these results, quality measures, such as coranking [LV09], have been proposed to quantify structural distortions that occur between highdimensional and lowdimensional data representations. Such measures could be evaluated and visualized pointwise to further highlight erroneous regions [MLGH13]. In this work, we provide an interactive visualization framework for exploring highdimensional data via its twodimensional embeddings obtained from dimension reduction, using a rich set of user interactions. We ask the following question: what new insights do we obtain regarding the structure of the data, with interactive manipulations of its embeddings in the visual space? We augment the twodimensional embeddings with structural abstractions obtained from hierarchical clusterings, to help users navigate and manipulate subsets of the data. We use pointwise distortion measures to highlight interesting regions in the domain, and further to guide our selection of the appropriate level of clusterings that are aligned with the regions of interest. Under the static setting, pointwise distortions indicate the level of structural uncertainty within the embeddings. Under the dynamic setting, onthefly updates of pointwise distortions due to data movement and data deletion reflect structural relations among different parts of the data, which may lead to new and valuable insights. 1.
SpectralBased Contractible Parallel Coordinates
"... Abstract—Parallel coordinates is wellknown as a popular tool for visualizing the underlying relationships among variables in highdimensional datasets. However, this representation still suffers from visual clutter arising from intersections among polyline plots especially when the number of data ..."
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Abstract—Parallel coordinates is wellknown as a popular tool for visualizing the underlying relationships among variables in highdimensional datasets. However, this representation still suffers from visual clutter arising from intersections among polyline plots especially when the number of data samples and their associated dimension become high. This paper presents a method of alleviating such visual clutter by contracting multiple axes through the analysis of correlation between every pair of variables. In this method, we first construct a graph by connecting axis nodes with an edge weighted by data correlation between the corresponding pair of dimensions, and then reorder the multiple axes by projecting the nodes onto the primary axis obtained through the spectral graph analysis. This allows us to compose a dendrogram tree by recursively merging a pair of the closest axes one by one. Our visualization platform helps the visual interpretation of such axis contraction by plotting the principal component of each data sample along the composite axis. Smooth animation of the associated axis contraction and expansion has also been implemented to enhance the visual readability of behavior inherent in the given highdimensional datasets. Keywordsparallel coordinates; axis contraction; spectral graph theory; dendrograms I.