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Distortion‐Guided Structure‐Driven Interactive Exploration of High‐Dimensional Data

https://doi.org/10.1111/cgf.12366
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Direct numerical simulation of ignition front propagation in a constant volume with temperature inhomogeneities
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Visualizing distortions and recovering topology in continuous projection techniques
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Scenario clustering and dynamic probabilistic risk assessment
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Extracting insights from the shape of complex data using topology
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Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics
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Visual Exploration of High Dimensional Scalar Functions
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Topological Spines: A Structure-preserving Visual Representation of Scalar Fields
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Dimension Projection Matrix/Tree: Interactive Subspace Visual Exploration and Analysis of High Dimensional Data
inferredcites →https://doi.org/10.1109/tvcg.2013.157openalex-snapshot
Explainers: Expert Explorations with Crafted Projections
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iVisClassifier: An interactive visual analytics system for classification based on supervised dimension reduction
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Dis-function: Learning distance functions interactively
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iPCA: An Interactive System for PCA‐based Visual Analytics
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A Framework for Exploring Multidimensional Data with 3D Projections
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A Global Geometric Framework for Nonlinear Dimensionality Reduction
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Nonlinear Dimensionality Reduction by Locally Linear Embedding
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Relative Errors for Deterministic Low-Rank Matrix Approximations
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PRINCIPAL MANIFOLDS AND GRAPHS IN PRACTICE: FROM MOLECULAR BIOLOGY TO DYNAMICAL SYSTEMS
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Robust Euclidean embedding
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Universal multi-dimensional scaling
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Persistence-based clustering in riemannian manifolds
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Simple and deterministic matrix sketching
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Persistence-Based Clustering in Riemannian Manifolds
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Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
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Data Visualization With Multidimensional Scaling
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Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition
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Knowledge Discovery and Data Mining
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Finding groups in data: an introduction to cluster analysis

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Visual Analytics for Classifier Construction and Evaluation for Medical Data
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Visual Analytics of Happiness Index In Parallel Coordinate Graph
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Multidimensional data visualization applying a variety-oriented scatterplot selection technique
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High-dimensional data visualization by interactive construction of low-dimensional parallel coordinate plots
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Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics
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Multidimensional Projection for Visual Analytics: Linking Techniques with Distortions, Tasks, and Layout Enrichment
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t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
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Measuring and Explaining the Inter-Cluster Reliability of Multidimensional Projections
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Attribute-based Explanation of Non-Linear Embeddings of High-Dimensional Data
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Scatterplot Selection Applying a Graph Coloring Problem
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Interpretation of Dimensionally-reduced Crime Data: A Study with Untrained Domain Experts
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