Local Affine Multidimensional Projection

Joia, Paulo; Paulovich, Fernando V.; Coimbra, Danilo Barbosa; Cuminato, José Alberto; Nonato, Luís Gustavo

doi:10.1109/tvcg.2011.220

Cited by 243 publications

(345 citation statements)

References 26 publications

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“…In order to show the usefulness of the proposed approach in combining and evaluating projections we have carried out a set of experiments taking as basis 5 distinct multidimensional projection techniques, namely, Least-Square Projection (LSP) [38], Part-Linear Multidimensional Projection (PLMP) [26], Local Affine Multidimensional Projection (LAMP) [35], Sammon's Mapping Speeding-up (SMS) [39], and the Hybrid Model (Hybrid) [40]. A common factor of all these techniques is that they all rely on control points placed in the visual space to perform the projections.…”

Section: Resultsmentioning

confidence: 99%

“…In our tool ProjInspector, we have implemented three quality metrics, namely, Kruskal's stress function [33], the correlation coefficient [34], and a modified, smooth variant of the neighborhood preservation metric [35]. These metrics gauge different aspects of the quality of a projection, as shown in our experiments.…”

Section: Quality Metricsmentioning

confidence: 99%

“…The conventional definition of a neighborhood preservation metric [35] computes the percentage of the k-nearest neighbors of an instance i that still remain neighbors in the visual space. Trustworthiness and continuity [36] are other measures that evaluate neighborhood matching in the original and visual space.…”

Section: Smooth Neighborhood Preservationmentioning

confidence: 99%

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Projection inspector: Assessment and synthesis of multidimensional projections

et al. 2015

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Section: Resultsmentioning

confidence: 99%

Section: Quality Metricsmentioning

confidence: 99%

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Projection inspector: Assessment and synthesis of multidimensional projections

et al. 2015

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“…We define the similarity between two projection glyphs as the Euclidean distance between two corresponding projection matrices, i.e., A 1 − A 2 2 . Thus, the layout of the glyphs is determined using a local affine multi-dimensional projection algorithm [42]. The user can plan a rotation path containing a sequence of projection glyphs among the multiple projection glyphs, then a rotation animation is generated based on interpolation between adjacent projections along the path [27] with a slider to control the position of the animation.…”

Section: Visual Exploration Of the Projected Scatterplotmentioning

confidence: 99%

EasySVM: A visual analysis approach for open-box support vector machines

Chen

et al. 2017

Comp. Visual Media

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Support vector machines (SVMs) are supervised learning models traditionally employed for classification and regression analysis. In classification analysis, a set of training data is chosen, and each instance in the training data is assigned a categorical class. An SVM then constructs a model based on a separating plane that maximizes the margin between different classes. Despite being one of the most popular classification models because of its strong performance empirically, understanding the knowledge captured in an SVM remains difficult. SVMs are typically applied in a black-box manner where the details of parameter tuning, training, and even the final constructed model are hidden from the users. This is natural since these details are often complex and difficult to understand without proper visualization tools. However, such an approach often brings about various problems including trial-and-error tuning and suspicious users who are forced to trust these models blindly.The contribution of this paper is a visual analysis approach for building SVMs in an open-box manner. Our goal is to improve an analyst's understanding of the SVM modeling process through a suite of visualization techniques that allow users to have full interactive visual control over the entire SVM training process. Our visual exploration tools have been developed to enable intuitive parameter tuning, training data 2016-12-24; accepted: 2017-01-09 manipulation, and rule extraction as part of the SVM training process. To demonstrate the efficacy of our approach, we conduct a case study using a real-world robot control dataset.

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“…The Local Affine Multidimensional Projection (LAMP) technique [28] is one example of a hybrid global and local approach. It starts by projecting a sample of instances to the transformed space and then interpolates the remaining instances through a family of orthogonal affine mappings, one for each instance to be projected.…”

Section: Related Workmentioning

confidence: 99%