The DOI matches its Crossref record; 298 of the 306 references it cites were found in a registry or an index.
Visualizing High-Dimensional Data: Advances in the Past Decade
Abstract
Massive simulations and arrays of sensing devices, in combination with increasing computing resources, have generated large, complex, high-dimensional 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 high-dimensional data visualization that focuses on the past decade. We aim at providing guidance for data practitioners to navigate through a modular view of the recent advances, inspiring the creation of new visualizations along the enriched visualization pipeline, and identifying future opportunities for visualization research.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“On the contrary, some papers related to the Software Design KA were not related to the Software Construction KA [12,13,19,20].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [13]
In the citing paper
“Several software processes for ML applications have been proposed [13,19,30,67,88,89].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [13]
Citations
Cites
(2008) · Journal of Machine Learning Research · Cited by 33,051
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
Jolliffe, I. T. (2005) · Wiley eBooks · doi:10.1002/0470013192.bsa501 · Cited by 14,522
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,723
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
David, F. N., & Tukey, J. W. (1977) · Biometrics · doi:10.2307/2529486 · Cited by 12,707
Goodall, C. R., & Jolliffe, I. T. (1988) · Technometrics · doi:10.2307/1270093 · Cited by 8,797
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Belkin, M., & Niyogi, P. (2003) · Neural Computation · doi:10.1162/089976603321780317 · Cited by 7,562
Multidimensional Scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis
Kruskal, J. B. (1964) · Psychometrika · doi:10.1007/bf02289565 · Cited by 7,270
Readings in Information Visualization: Using Vision to Think
Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999) · Cited by 3,935
Research works printed but not linked: 5
- liu, 2015, Visualizing high-dimensional data: Advances in the past decade, Proc of the EuroGraphics conf State of the Art Report, 127
- reeb, 1946, Sur les points singuliers d'une forme de pfaff completement intergrable ou d'une fonction numerique [on the singular points of a complete integral pfaff form or of a numerical function], Comptes Rendus Acad Sci Paris, 222, 847
- geng, 2015, Topological analysis of atlantic ocean data using joint contour net, Proc Topology-Based Methods Vis
- bürger, 2007, Visualization of multi-variate scientific data, Eurographics State of the Art Reports, 117
- edelsbrunner, 2002, Jacobi sets of multiple morse functions, Found Comput Math, 312, 37
Cited by
Sorted by citations
Items 401–425 of 425
Vu, L., Frank, M., Xu, H., Chen, S., Nguyen, T. N., He, S., Deng, B., & Suo, K. (2025) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/ipccc66453.2025.11304626
Fast extremum graph computation for large-scale arbitrary grids
Li, Q., Liu, Z., Huang, Z., Li, W., Chu, Z., Dai, Z., Yu, M., & Deng, F. (2026) · Computer Aided Geometric Design · doi:10.1016/j.cagd.2026.102519
Human-AI Interaction and Visualization Perspectives on ADR
Kucher, K., Bång, M., & Lundberg, J. (2026) · Springer eBooks · doi:10.1007/978-3-032-10561-5_22 · Cited by 1
Data Science and Data Visualization
Xyntarakis, M., & Antoniou, C. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26789-5.00023-7
Derksen, M., Dieke, V., Kuhlen, T., Botsch, M., & Weißker, T. (2026) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr67842.2026.00029
Exploration et modélisation de données peu ou pas structurées
Grollemund, V. (2021) · theses.fr (ABES) · doi:10.70675/03f0bf21z8b4az4f15z9a7bz36e850e62608
Comparing high-dimensional data : interpretable two-sample testing by variable selection
Mitsuzawa, K. (2024) · theses.fr (ABES) · doi:10.70675/2077c87az8302z4b6fz9922z2c300be3335e
Loslever, P., Schiro, J., Gabrielli, F., & Pudlo, P. (2026) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2026.2673232
Local Semantics Refinement of Adaptive Representations for Robust Noisy Label Learning
Lin, Y., Zhang, Y., Hou, J., Wang, Z., & Zeng, Z. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3696107
Glyphspace: An Integrated Browser-Based Workflow for Glyph-Enriched Dimensionality Reduction
Kammer, D., Keck, M., Schneider, L., & Gründer, T. (2026) · International Conference on Advanced Visual Interfaces (AVI) · doi:10.1145/3811427.3811504
HamCat: Ego‐Centric Relationship Exploration for Multidimensional Categorical Data
Balaka, H., Hauser, H., & Garrison, L. A. (2026) · Computer Graphics Forum · doi:10.1111/cgf.70440
Homology-Preserving Dimensionality Reduction via Adaptive Mapper and Landmark Isomap
Khourashahi, S., Jahanshahi, I., Wang, B., & Yan, L. (2026) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2026.3703433
Self-citationAutoencoder-based regularization methods for parametric and inverse projectionsPreprint
Metz, Y., Dennig, F. L., Blumberg, D., & Geyer, N. (2026) · OSF Preprints (OSF Preprints)
Zhang, B., He, P., & Jin, N. (2026) · International Symposium on Computer Technology and Information Science (ISCTIS) · doi:10.1109/isctis70043.2026.11572251
Towards Intelligent Visual Data Mining: An Exploratory Learning Approach
Paturu, M. K., & Ashok Kumar, T. (2026) · International Conference on Innovations in Cybersecurity and Data Science (ICICDS) · doi:10.1109/icicds70526.2026.11604836
Kovač, N., Ratković, K., Simeunović, M., Farahani, H., & Zhao, L. (2026) · Springer eBooks · doi:10.1007/978-3-032-18312-5_12
Visual analytics of transfer learning with LSTM for modeling multi-condition industrial processes
Ji, L., Xu, Z., Tian, W., Yao, X., Fu, Y., Yang, G., & Tian, B. (2026) · Visual Informatics · doi:10.1016/j.visinf.2026.100366
Zerkowski, L. V., & Hirata, N. S. T. (2026) · Journal on Computing and Cultural Heritage · doi:10.1145/3849813
Glyph-based visualization with highlighting for analyzing longitudinal machine learning experiments
Meng, L., Chakraborty, J., van den Elzen, S., Vilanova, A., & Chen, M. (2026) · Computers & Graphics · doi:10.1016/j.cag.2026.104772
Advanced visualization workflows for gyrokinetics simulation data
Costa, R., Hornsby, W., Kennedy, D., Zanisi, L., Bhatia, N., Pamela, S. J. P., Gonzalez-Beltran, A. N., & Akers, R. (2026) · Frontiers in Physics · doi:10.3389/fphy.2026.1924612
МОДЕЛЬ ВЕБОРІЄНТОВАНОГО МЕХАНІЗМУ ОЧИЩЕННЯ ТА НОРМАЛІЗАЦІЇ ВІДКРИТИХ ДАНИХ ДЛЯ ВІЗУАЛІЗАЦІЇ
Романюк, Р. Р., & Мисник, Б. В. (2026) · ГРААЛЬ НАУКИ · doi:10.36074/grail-of-science.16.10.2026.038
Valdrighi, G., Garcia-Zanabria, G., Diaz, M., Nonato, L. G., Júnior, N. R. F., & Poco, J. (2026) · Computer Graphics Forum · doi:10.1111/cgf.70653
Mapping Fiscal Landscapes: Understanding of Public Sector Finances Through Data Visualisation
Jangam, A. S., Rahman, M. A., Shopland, N., Osman, T., & Brown, D. J. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-42348-1_31
Metadata
- Authors
- Liu, S., Maljovec, D., Wang, B., Bremer, P.-T., & Pascucci, V.
- Affiliations
- University of Utah (US)
- Lawrence Livermore National Laboratory (US)
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2017-03-01
- Volume
- 23
- Issue
- 3
- Pages
- 1249-1268
- ISSN
- 1077-2626 (ISSN-L)
- OpenAlex
- W2323909273
- Cited by
- 427
- References
- 306
- Citations by year
- 2026: 25 · 2025: 34 · 2024: 45 · 2023: 46 · 2022: 45 · 2021: 52 · 2020: 62 · 2019: 49 · 2018: 32 · 2017: 21
- FWCI
- 26.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Visualization and Computer Graphics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- US DOE
- LLNL (DE-AC52-07NA27344, LLNL-CONF-658933)
- US National Science Foundation (IIS-1513616, 0904631, DE-EE0004449, DE-NA0002375, DE-SC0007446, DE-SC0010498, NSG IIS-1045032, NSF EFT ACI-0906379, DOE/NEUP 120341, DOE/Codesign P01180734)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Keywords
- high-dimensional data visualization, dimensionality reduction, visualization pipeline, large-scale datasets
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
