Orthographic Star Coordinates

Lehmann, Dirk J.; Theisel, Holger

doi:10.1109/tvcg.2013.182

Cited by 44 publications

(37 citation statements)

References 23 publications

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“…For instance, these can be grouped into global (scatterplots, PCA, star coordinates [16,17], orthographic star coordinates [18], biplots [19,20], radial visualizations [21,22]), and local (LAMP, LLE [23], t-SNE [12]). Global techniques use the same transformation to project all points to the target (2D) space.…”

Section: Element-based Plotsmentioning

confidence: 99%

Multidimensional Data Exploration by Explicitly Controlled Animation

et al. 2017

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Abstract:Understanding large multidimensional datasets is one of the most challenging problems in visual data exploration. One key challenge that increases the size of the exploration space is the number of views that one can generate from a single dataset, based on the use of multiple parameter values and exploration paths. Often, no such single view contains all needed insights. The question thus arises of how we can efficiently combine insights from multiple views of a dataset. We propose a set of techniques that considerably reduce the exploration effort for such situations, based on the explicit depiction of the view space, using a small multiple metaphor. We leverage this view space by offering interactive techniques that enable users to explicitly create, visualize, and follow their exploration path. This way, partial insights obtained from each view can be efficiently and effectively combined. We demonstrate our approach by applications using real-world datasets from air traffic control, software maintenance, and machine learning.

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Section: Element-based Plotsmentioning

confidence: 99%

Multidimensional Data Exploration by Explicitly Controlled Animation

et al. 2017

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“…Projective projections map straight lines to straight lines and preserve the cross ratio of four collinear points [Far02,LT13]. Affine projections map straight lines to straight lines and preserve the ratio of three collinear points [Far02,LT13]. It introduces non-linearity in the projection process by using an additional weighting factor based on an underlying spring model.…”

Section: Related Workmentioning

confidence: 99%

General Projective Maps for Multidimensional Data Projection

Lehmann¹,

Theisel

2016

Computer Graphics Forum

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To project high-dimensional data to a 2D domain, there are two well-established classes of approaches: RadViz and Star Coordinates. Both are well-explored in terms of accuracy, completeness, distortions, and interaction issues. We present a generalization of both RadViz and Star Coordinates such that it unifies both approaches. We do so by considering the space of all projective projections. This gives additional degrees of freedom, which we use for three things: Firstly, we define a smooth transition between RadViz and Star Coordinates allowing the user to exploit the advantages of both approaches. Secondly, we define a data-dependent magic lens to explore the data. Thirdly, we optimize the new degrees of freedom to minimize distortion. We apply our approach to a number of high-dimensional benchmark datasets.

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“…They project an ndimensional data record m onto a two-dimensional point p using a 2 × n projection matrix A, i.e., p = A· m. If the projection is orthographic and multivariate, then Orthographic Star Coordinates (OSC) [LT13] are given. They project an ndimensional data record m onto a two-dimensional point p using a 2 × n projection matrix A, i.e., p = A· m. If the projection is orthographic and multivariate, then Orthographic Star Coordinates (OSC) [LT13] are given.…”

Section: Projection Techniques Considered For the Design Of Visual Gumentioning

confidence: 99%

“…They project an ndimensional data record m onto a two-dimensional point p using a 2 × n projection matrix A, i.e., p = A· m. If the projection is orthographic and multivariate, then Orthographic Star Coordinates (OSC) [LT13] are given. [LT13]). 3 (bottom), with radius r = 1 for the RadVis and the Star Coordinates, and radius r = 2/n for the Orthographic Star Coordinates (cf.…”

Section: Projection Techniques Considered For the Design Of Visual Gumentioning

confidence: 99%

Visualnostics: Visual Guidance Pictograms for Analyzing Projections of High‐dimensional Data

Lehmann¹,

Kemmler²,

Zhyhalava³

et al. 2015

Computer Graphics Forum

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The visual analysis of multivariate projections is a challenging task, because complex visual structures occur. This causes fatigue or misinterpretations, which distorts the analysis. In fact, the same projection can lead to different analysis results. We provide visual guidance pictograms to improve objectivity of the visual search. A visual guidance pictogram is an iconic visual density map encoding the visual structure of certain data properties. By using them to guide the analysis, structures in the projection can be better understood and mentally linked to properties in the data. We introduce a systematic scheme for designing such pictograms and provide a set of pictograms for standard visual tasks, such as correlation and distribution analysis, for standard projections like scatterplots, RadVis, and Star Coordinates. We conduct a study that compares the visual analysis of real data with and without the support of guidance pictograms. Our tests show that the training effort for a visual search can be decreased and the analysis bias can be reduced by supporting the user's visual search with guidance pictograms.

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Orthographic Star Coordinates

Cited by 44 publications

References 23 publications

Multidimensional Data Exploration by Explicitly Controlled Animation

Multidimensional Data Exploration by Explicitly Controlled Animation

General Projective Maps for Multidimensional Data Projection

Visualnostics: Visual Guidance Pictograms for Analyzing Projections of High‐dimensional Data

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