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
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
Hohman, F., Kahng, M., Pienta, R., & Chau, D. H. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2843369 · Cited by 582
Zio, E. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.04.020 · Cited by 456
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
Wang, Z. J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., Kahng, M., & Chau, D. H. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030418 · Cited by 362
Generative Adversarial Networks for face generation: A survey
Kammoun, A., Slama, R., Tabia, H., Ouni, T., & Abid, M. (2022) · ACM Computing Surveys · doi:10.1145/1122445.1122456 · Cited by 370
Toward a Quantitative Survey of Dimension Reduction Techniques
Espadoto, M., Martins, R. M., Kerren, A., Hirata, N. S. T., & Telea, A. C. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2944182 · Cited by 348
Visualizing the Hidden Activity of Artificial Neural Networks
Rauber, P. E., Fadel, S. G., Falcao, A. X., & Telea, A. C. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598838 · Cited by 280
Making data visualization more efficient and effective: a survey
Qin, X., Luo, Y., Tang, N., & Li, G. (2019) · The VLDB Journal · doi:10.1007/s00778-019-00588-3 · Cited by 278
Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis
Sacha, D., Zhang, L., Sedlmair, M., Lee, J. A., Peltonen, J., Weiskopf, D., North, S. C., & Keim, D. A. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598495 · Cited by 232
GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
Kahng, M., Thorat, N., Chau, D. H., Viégas, F. B., & WATTENBERG, M. P. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864500 · Cited by 206
A Survey of Topology‐based Methods in Visualization
Heine, C., Leitte, H., Hlawitschka, M. W., Iuricich, F., Floriani, L. D., Scheuermann, G., Hagen, H., & Garth, C. (2016) · Computer Graphics Forum · doi:10.1111/cgf.12933 · Cited by 165
A survey of surveys on the use of visualization for interpreting machine learning models
Chatzimparmpas, A., Martins, R. M., Jusufi, I., & Kerren, A. (2020) · Information Visualization · doi:10.1177/1473871620904671 · Cited by 164
Feature Selective Projection with Low-Rank Embedding and Dual Laplacian Regularization
Tang, C., Liu, X., Zhu, X., Xiong, J., Li, M., Xia, J., Wang, X., & Wang, L. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2911946 · Cited by 144
Visual analytics in urban computing: An overview
Zheng, Y., Wu, W., Chen, Y., Qu, H., & Ni, L. M. (2016) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2586447 · Cited by 141
Personal Augmented Reality for Information Visualization on Large Interactive Displays
Reipschläger, P., Flemisch, T., & Dachselt, R. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030460 · Cited by 140
Visualizing Time-Dependent Data Using Dynamic t-SNE
Rauber, P., Falcão, A. X., & Telea, A. C. (2016) · Queen Mary Research Online (Queen Mary University of London) · doi:10.2312/eurovisshort.20161164 · Cited by 137
Visual Analytics: A Comprehensive Overview
Cui, W. (2019) · IEEE Access · doi:10.1109/access.2019.2923736 · Cited by 133
Visual Analysis and Dissemination of Scientific Literature Collections with SurVis
Beck, F., Koch, S., & Weiskopf, D. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2467757 · Cited by 110
Towards Building a Plant Cell Atlas
Rhee, S. Y., Birnbaum, K. D., & Ehrhardt, D. W. (2019) · Trends in Plant Science · doi:10.1016/j.tplants.2019.01.006 · Cited by 110
Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics
Wenskovitch, J. E., Crandell, I., Ramakrishnan, N., House, L., Leman, S. C., & North, C. L. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2745258 · Cited by 101
Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning
Fujiwara, T., Kwon, O.-H., & Ma, K. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934251 · Cited by 90
Survey of Surveys (SoS) ‐ Mapping The Landscape of Survey Papers in Information Visualization
McNabb, L., & Laramee, R. S. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13212 · Cited by 82
Software engineering challenges for machine learning applications: A literature review
Kumeno, F. (2020) · Intelligent Decision Technologies · doi:10.3233/idt-190160 · Cited by 81
McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data
Mongia, A., Sengupta, D., & Majumdar, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00009 · Cited by 77
Towards Better Bus Networks: A Visual Analytics Approach
Weng, D., Zheng, C., Deng, Z., Ma, M., Bao, J., Zheng, Y., Xu, M., & Wu, Y. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030458 · Cited by 75
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data
Fujiwara, T., Chou, J., Shilpika, Xu, P., Ren, L., & Ma, K. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934433 · Cited by 69
LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets
Xia, J., Ye, F., Chen, W., Wang, Y., Chen, W., Ma, Y., & Tung, A. K. H. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2744098 · Cited by 69
Anomaly Detection in High-Dimensional Data
Talagala, P. D., Hyndman, R. J., & Smith‐Miles, K. (2020) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2020.1807997 · Cited by 77
Immersive Insights: A Hybrid Analytics System forCollaborative Exploratory Data Analysis
Cavallo, M., Dolakia, M., Havlena, M., Ocheltree, K., & Podlaseck, M. (2019) · ACM Symposium on Virtual Reality Software and Technology (VRST) · doi:10.1145/3359996.3364242 · Cited by 64
Dimensionality reduction for visualizing industrial chemical process data
Joswiak, M. N., Peng, Y., Castillo, I., & Chiang, L. H. (2019) · Control Engineering Practice · doi:10.1016/j.conengprac.2019.104189 · Cited by 65
Porter, M. M., & Niksiar, P. (2018) · PLoS ONE · doi:10.1371/journal.pone.0204309 · Cited by 65
Boggust, A., Carter, B., & Satyanarayan, A. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511122 · Cited by 73
A survey of immersive visualization: Focus on perception and interaction
Zhang, Y., Wang, Z.-Y., Zhang, J., Shan, G., & Tian, D. (2023) · Visual Informatics · doi:10.1016/j.visinf.2023.10.003 · Cited by 58
Projections as visual aids for classification system design
Rauber, P., Falcão, A. X., & Telea, A. C. (2017) · Information Visualization · doi:10.1177/1473871617713337 · Cited by 58
A task-and-technique centered survey on visual analytics for deep learning model engineering
García, R., Telea, A. C., da Silva, B. C., Tørresen, J., & Comba, J. L. D. (2018) · Computers & Graphics · doi:10.1016/j.cag.2018.09.018 · Cited by 55
A comprehensive review of tools for exploratory analysis of tabular industrial datasets
Ghosh, A., Nashaat, M., Miller, J., Quader, S., & Marston, C. (2018) · Visual Informatics · doi:10.1016/j.visinf.2018.12.004 · Cited by 52
Evaluating the Impact of Binning 2D Scalar Fields
Padilla, L., Quinan, P. S., Meyer, M., & Creem-Regehr, S. H. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2599106 · Cited by 50
Fujiwara, T., Shilpika, Sakamoto, N., Nonaka, J., Yamamoto, K., & Ma, K. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3028889 · Cited by 50
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Houben, S., Abrecht, S., Akila, M., Bär, A., Brockherde, F., Feifel, P., Fingscheidt, T., Gannamaneni, S. S., Ghobadi, S. E., Hammam, A., et al. (2022) · Springer eBooks · doi:10.1007/978-3-031-01233-4_1 · Cited by 62
Uncertainty-Aware Principal Component Analysis
Gortler, J., Spinner, T., Streeb, D., Weiskopf, D., & Deussen, O. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934812 · Cited by 40
Interactive Dimensionality Reduction for Comparative Analysis
Fujiwara, T., Wei, X., Zhao, J., & Ma, K. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114807 · Cited by 53
Digital Twin for Aquaponics Factory: Analysis, Opportunities, and Research Challenges
Xu, L. P., Yu, H., Qin, H., Chai, Y., Yan, N., Li, D., & Chen, Y. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3331135 · Cited by 46
Taggle: Combining overview and details in tabular data visualizations
Furmanová, K., Gratzl, S., Stitz, H., Zichner, T., Jarešová, M., Lex, A., & Streit, M. (2019) · Information Visualization · doi:10.1177/1473871619878085 · Cited by 51
Alsenan, S. A., Al-Turaiki, I. M., & Hafez, A. M. (2020) · IEEE Access · doi:10.1109/access.2020.2990375 · Cited by 45
Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots
Marai, G. E., Ma, C., Burks, A. T., Pellolio, F., Canahuate, G. M., Vock, D. M., Mohamed, A., & Fuller, C. D. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2817557 · Cited by 42
Diagnosing Concept Drift with Visual Analytics
Yang, W., Li, Z., Liu, M., Lu, Y., Cao, K., Maciejewski, R., & Liu, S. (2020) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast50239.2020.00007 · Cited by 40
Self-citationLinear discriminant analysis guided by unsupervised ensemble learning
Deng, P., Wang, H., Li, T., Horng, S., & Zhu, X. (2018) · Information Sciences · doi:10.1016/j.ins.2018.12.036 · Cited by 39
Visualizations for decision support in scenario-based multiobjective optimization
Shavazipour, B., López‐Ibáñez, M., & Miettinen, K. (2021) · Information Sciences · doi:10.1016/j.ins.2021.07.025 · Cited by 39
Visualizing probabilistic models and data with Intensive Principal Component Analysis
Quinn, K. N., Clement, C. B., Bernardis, F. D., Niemack, M. D., & Sethna, J. P. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1817218116 · Cited by 37
Kammer, D., Keck, M., Gründer, T., Maasch, A., Thom, T., Kleinsteuber, M., & Groh, R. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.2969060 · Cited by 38
ARGUS: Visualization of AI-Assisted Task Guidance in AR
Castelo, S., Rulff, J., McGowan, E., Steers, B., Wu, G., Chen, S., Roman, I. R., López, R., Brewer, E., Zhao, C., et al. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3327396 · Cited by 41
Constructing and Visualizing High-Quality Classifier Decision Boundary Maps
Rodrigues, F., Espadoto, M., Hirata, R., & Telea, A. C. (2019) · Information · doi:10.3390/info10090280 · Cited by 39
Differentially Private High-Dimensional Data Publication in Internet of Things
Zheng, Z., Wang, T., Wen, J., Mumtaz, S., Bashir, A. K., & Chauhdary, S. H. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2955503 · Cited by 37
Perkhofer, L., Walchshofer, C., & Hofer, P. (2020) · Journal of Management Control · doi:10.1007/s00187-020-00294-0 · Cited by 37
Castro, S. C., Quinan, P. S., Hosseinpour, H., & Padilla, L. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114803 · Cited by 40
Simulation-based exploration of high-dimensional system models for identifying unexpected events
Turati, P., Pedroni, N., & Zio, E. (2017) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2017.04.004 · Cited by 34
embComp : Visual Interactive Comparison of Vector Embeddings
Heimerl, F., Kralj, C., Möller, T. B., & Gleicher, M. L. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3045918 · Cited by 34
Considerations when processing and interpreting genomics data of the placenta
Konwar, C., Gobbo, G. F. D., Yuan, V., & Robinson, W. P. (2019) · Placenta · doi:10.1016/j.placenta.2019.01.006 · Cited by 33
VisAdapt: A Visualization Tool to Support Climate Change Adaptation
Johansson, J., Opach, T., Glaas, E., Neset, T., Navarra, C., Linnér, B., & Rød, J. K. (2016) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2016.49 · Cited by 32
Quantitative Evaluation of Time‐Dependent Multidimensional Projection Techniques
Vernier, E. F., García, R., da Silva, I. P., Comba, J. L. D., & Telea, A. C. (2020) · Computer Graphics Forum · doi:10.1111/cgf.13977 · Cited by 32
Self-organizing subspace clustering for high-dimensional and multi-view data
Araujo, A. F. R., Antonino, V. O., & Ponce-Guevara, K. L. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.06.022 · Cited by 31
Diagnosing Ensemble Few-Shot Classifiers
Yang, W., Ye, X., Zhang, X., Xiao, L., Xia, J., Wang, Z., Zhu, J. J., Pfister, H., & Liu, S. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3182488 · Cited by 34
Self-citationUnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data
Espadoto, M., Appleby, G., Suh, A., Cashman, D., Li, M., Scheidegger, C., Anderson, E. W., Chang, R., & Telea, A. C. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3125576 · Cited by 29
Cluster-Based Visual Abstraction for Multivariate Scatterplots
Liao, H., Wu, Y., Chen, L., & Chen, W. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2754480 · Cited by 29
Data Abstraction for Visualizing Large Time Series
Shurkhovetskyy, G., Andrienko, N. V., Andrienko, G., & Fuchs, G. (2017) · Computer Graphics Forum · doi:10.1111/cgf.13237 · Cited by 29
Dual graph regularized compact feature representation for unsupervised feature selection
Li, S., Tang, C., Liu, X., Liu, Y., & Chen, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.11.060 · Cited by 29
Orban, D., Keefe, D. F., Biswas, A., Ahrens, J. P., & Rogers, D. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2865051 · Cited by 28
The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets
Afzal, S., Hittawe, M. M., Ghani, S., Jamil, T., Knio, O., Hadwiger, M., & Hoteit, I. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13731 · Cited by 30
Kelleher, C. A., & Braswell, A. (2021) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2021.105113 · Cited by 30
VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics
Huang, Z., Witschard, D., Kucher, K., & Kerren, A. (2023) · Computer Graphics Forum · doi:10.1111/cgf.14859 · Cited by 37
Structuring Visual Exploratory Analysis of Skill Demand
Dadzie, A., Sibarani, E. M., Novalija, I., & Scerri, S. (2017) · Journal of Web Semantics · doi:10.1016/j.websem.2017.12.004 · Cited by 28
TopoAct: Visually Exploring the Shape of Activations in Deep Learning
Rathore, A., Chalapathi, N., Palande, S., & Wang, B. (2021) · Computer Graphics Forum · doi:10.1111/cgf.14195 · Cited by 37
Self-citationAttribute-based Explanation of Non-Linear Embeddings of High-Dimensional Data
Sohns, J., Schmitt, M., Jirasek, F., Hasse, H., & Leitte, H. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114870 · Cited by 27
Visualizing High Dimensional and Big Data
Genender-Feltheimer, A. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.10.308 · Cited by 27
Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data
Borland, D., Wang, W., Zhang, J., Shrestha, J., & Gotz, D. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934209 · Cited by 26
PolarViz: a discriminating visualization and visual analytics tool for high-dimensional data
Wang, Y. C., Zhang, Q., Lin, F., Goh, C.-K., & Seah, H. S. (2018) · The Visual Computer · doi:10.1007/s00371-018-1558-y · Cited by 25
Suh, J., Ghorashi, S., Ramos, G., Chen, N.-C., Drucker, S. M., Verwey, J., & Simard, P. (2019) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3241379 · Cited by 29
Generalized persistence algorithm for decomposing multiparameter persistence modules
Dey, T. K., & Xin, C. (2022) · Journal of Applied and Computational Topology · doi:10.1007/s41468-022-00087-5 · Cited by 24
Cutura, R., Aupetit, M. J. M., Fekete, J., & Sedlmair, M. (2020) · International Conference on Advanced Visual Interfaces (AVI) · doi:10.1145/3399715.3399875 · Cited by 24
Indexed-Points Parallel Coordinates Visualization of Multivariate Correlations
Zhou, L., & Weiskopf, D. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2698041 · Cited by 22
ECoalVis: Visual Analysis of Control Strategies in Coal-fired Power Plants
Liu, S., Weng, D., Tian, Y., Deng, Z., Xu, H., Zhu, X., Yin, H., Zhan, X., & Wu, Y. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209430 · Cited by 21
Self-citationDMT-EV: An Explainable Deep Network for Dimension Reduction
Zang, Z., Cheng, S., Xia, H., Li, L., Sun, Y., Xu, Y., Shang, L., Sun, B., & Li, S. Z. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3223399 · Cited by 23
Uniform Manifold Approximation with Two-phase Optimization
Jeon, H., Ko, H.-K., Lee, S., Jo, J., & Seo, J. (2022) · Visualization · doi:10.1109/vis54862.2022.00025 · Cited by 21
Testing the potential of Multiobjective Evolutionary Algorithms (MOEAs) with Colorado water managers
Smith, R. M., Kasprzyk, J. R., & Dilling, L. (2019) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2019.03.011 · Cited by 21
Blumenschein, M., Behrisch, M., Schmid, S., Butscher, S., Wahl, D. R., Villinger, K., Renner, B., Reiterer, H., & Keim, D. A. (2018) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2018.8802486 · Cited by 20
Visual Analysis of Class Separations With Locally Linear Segments
Ma, Y., & Maciejewski, R. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3011155 · Cited by 20
Self-supervised Dimensionality Reduction with Neural Networks and Pseudo-labeling
Espadoto, M., Hirata, N. S. T., & Telea, A. C. (2021) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010184800270037 · Cited by 20
Ränger, L., von Kurnatowski, M., Bortz, M., & Grützner, T. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.107059 · Cited by 19
Xie, C., Zhong, W., & Mueller, K. D. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598479 · Cited by 18
Dimensionality reduction techniques in structural and earthquake engineering
Hajibabaee, P., Pourkamali‐Anaraki, F., & Hariri‐Ardebili, M. A. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2022.115485 · Cited by 19
Interactive Visualization on Large High‐Resolution Displays: A Survey
Belkacem, I., Tominski, C., Médoc, N., Knudsen, S., Dachselt, R., & Ghoniem, M. (2024) · Computer Graphics Forum · doi:10.1111/cgf.15001 · Cited by 18
ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in Virtual Screening
Sabando, M. V., Ulbrich, P., Selzer, M. N., Byška, J., Mičan, J., Ponzoni, I., Soto, A. J., Ganuza, M. L., & Kozlíková, B. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030438 · Cited by 23
Parallel t-SNE Applied to Data Visualization in Smart Cities
Lopes, M. A. D. S., Neto, A. D. D., & Martins, A. D. M. (2020) · IEEE Access · doi:10.1109/access.2020.2964413 · Cited by 17
TopoBERT: Exploring the topology of fine-tuned word representations
Rathore, A., Zhou, Y., Srikumar, V., & Wang, B. (2023) · Information Visualization · doi:10.1177/14738716231168671 · Cited by 18
Self-citationHuman-computer interaction for virtual-real fusion
Tao, J., Gong, J., Gao, N., Fu, S., Liang, S., & Yu, C. (2023) · Journal of Image and Graphics · doi:10.11834/jig.230020 · Cited by 18
The moving target of visualization software for an increasingly complex world
Reina, G., Childs, H., Matković, K., Bühler, K., Waldner, M., Pugmire, D., Kozlíková, B., Ropinski, T., Ljung, P., Itoh, T., et al. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.01.005 · Cited by 18
Strategies for evaluating visual analytics systems: A systematic review and new perspectives
Islam, M. R., Prome, S. A., Islam, L., Razzak, I., Wang, X., & Xu, G. (2023) · Information Visualization · doi:10.1177/14738716231212568 · Cited by 16
Comparative Analysis of Tools for Big Data Visualization and Challenges
Zion, G. D., & Tripathy, B. K. (2020) · Springer eBooks · doi:10.1007/978-981-15-2282-6_3 · Cited by 16
Salekin, S., Mostavi, M., Chiu, Y., Chen, Y., Zhang, J., & Huang, Y. (2020) · Frontiers in Physics · doi:10.3389/fphy.2020.00196 · Cited by 16
Humane Visual AI: Telling the Stories Behind a Medical Condition
So, W., Bogucka, E. P., Šćepanović, S., Joglekar, S. P., Zhou, K., & Quercia, D. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030391 · Cited by 16
Deep Fraud Detection on Non-attributed Graph
Wang, C., Dou, Y., Chen, M., Chen, J., Liu, Z., & Yu, P. S. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9672028 · Cited by 18
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
