Distortion-guided structure-driven interactive exploration of high-dimensional data

Computer Graphics Forum

Volumn 33, Issue 3, 2014, Pages 101-110

  Liu, S ^a   Wang, B ^a   Bremer, P T ^b   Pascucci, V ^a  

^a UNIVERSITY OF UTAH   (United States)

^b LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; DATA VISUALIZATION; FEATURE EXTRACTION; REDUCTION; VISUALIZATION;

COMPUTATIONALLY EFFICIENT; DIMENSION REDUCTION TECHNIQUES; INTERACTIVE EXPLORATION; INTERACTIVE MANIPULATION; INTERACTIVE VISUALIZATIONS; LOW-DIMENSIONAL REPRESENTATION; STRUCTURAL DISTORTIONS; STRUCTURAL UNCERTAINTY;

DATA MINING;

EID: 84904416799     PISSN: 01677055     EISSN: 14678659     Source Type: Journal    
DOI: 10.1111/cgf.12366     Document Type: Article

References (34)

1. Agarwal A., Phillips J.M., Venkatasubramanian S.,: Universal multi-dimensional scaling. Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (2010), 1149-1158. 4
2. Aupetit M.,: Visualizing distortions and recovering topology in continuous projection techniques. Neurocomputing 70 (2007), 1304-1330. 2
3. Brown E.T., Liu J., Brodley C.E., Chang R.,: Dis-function: Learning distance functions interactively. Proc. IEEE Conf. on Visual Analytics Science and Technology (2012), 83-92. 3
4. Belkin M., Niyogi P.,: Laplacian Eigenmaps for dimensionality reduction and data representation. Neural Computation 15, 6 (2003), 1373-1396. 2, 4
5. Buja A., Swayne D.F., Littman M.L., Dean N., Hofmann H., Chen L.,: Data visualization with multidimensional scaling. J. Comp. Graph. Stat. 17, 2 (2008), 444-472. 2
6. Correa C., Bremer P.-T., Lindstrom P.,: Topological spines: A structure-preserving visual representation of scalar fields. IEEE Trans. Vis. Comput. Graphics 17, 12 (2011), 1842-1851. 3
7. Cayton L., Dasgupta S.,: Robust Euclidean embedding. Proc. Int. Conf. on Machine learning (2006), 169-176. 4
8. Chazal F., Guibas L.J., Oudot S.Y., Skraba P.,: Persistence-based clustering in riemannian manifolds. Proc. ACM Symp. on Computational Geometry (2011), 97-106. 3
9. Choo J., Lee H., Kihm J., Park H.,: iVisClassifier: An interactive visual analytics system for classification based on supervised dimension reduction. Proc. IEEE Conf. on Visual Analytics Science and Technology (2010), 27-34. 3
10. Defays D.,: An efficient algorithm for a complete link method. Comput. J. 20, 4 (1977), 364-366. 5
11. France S., Carroll D.,: Development of an agreement metric based upon the rand index for the evaluation of dimensionality reduction techniques, with applications to mapping customer data. Machine Learning and Data Mining in Pattern Recognition, Lect. Notes Comput. Sc. 4571 (2007), 499-517. 2
12. Gärtner B.,: Fast and robust smallest enclosing balls. Algorithms-ESA'99, Lect. Notes Comput. Sc. (1999), 325-338. 4
13. Gerber S., Bremer P.-T., Pascucci V., Whitaker R.,: Visual exploration of high dimensional scalar functions. IEEE Trans. Vis. Comput. Graphics 16, 6 (2010), 1271-1280. 3, 8
14. Gerber S., Bremer P.-T., Pascucci V., Whitaker R.T.,: Visual exploration of high dimensional scalar functions. IEEE Trans. Vis. Comput. Graphics 16, 6 (2010), 1271-1280. 5, 7
15. Gleicher M.,: Explainers: Expert explorations with crafted projections. IEEE Trans. Vis. Comput. Graphics 19, 12 (2013), 2042-2051. 3
16. Ghashami M., Phillips J.M.,: Relative errors for deterministic low-rank matrix approximations. Proc. ACM-SIAM Symp. on Discrete Algorithms (2014). 9
17. Gorban A., Zinovyev A.,: Principal manifolds and graphs in practice: from molecular biology to dynamical systems. Int. J. Neural. Syst. 20, 3 (2010), 219-232. 2
18. Hawkes E.R., Sankaran R., Pébay P.P., Chen J.H.,: Direct numerical simulation of ignition front propagation in a constant volume with temperature inhomogeneities: II. Parametric study. Combust. Flame 145 (2006), 145-159. 7
19. Johansson S., Johansson J.,: Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE Trans. Vis. Comput. Graphics 15, 6 (2009), 993-1000. 3
20. Jeong D.H., Ziemkiewicz C., Fisher B., Ribarsky W., Chang R.,: iPCA: An interactive system for pcabased visual analytics. Comput. Graph. Forum 28, 3 (2009), 767-774. 3
21. Liberty E.,: Simple and deterministic matrix sketching. Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (2013). 9
22. Lum P.Y., Singh G., Lehman A., Ishkanov T., Vejdemo-Johansson M., Alagappan M., Carlsson J., Carlsson G.,: Extracting insights from the shape of complex data using topology. Sci. Rep. 3 (2013). 3
23. Lee J.A., Verleysen M.,: Quality assessment of dimensionality reduction: Rank-based criteria. Neurocomputing 72, 7 (2009), 1431-1443. 1, 2, 3, 4
24. Lisitsyn S., Widmer C., Garcia F. J. I.,: Tapkee: An efficient dimension reduction library. J. Mach. Learn. Res. 14 (2013), 2355-2359. 7
25. Mokbel B., Lueks W., Gisbrecht A., Hammer B.,: Visualizing the quality of dimensionality reduction. Neurocomputing 112 (2013), 109-123. 1, 2, 3, 4
26. Mandelli D., Yilmaz A., Aldemir T., Metzroth K., Denning R.,: Scenario clustering and dynamic probabilistic risk assessment. Reliab. Eng. Syst. Safe. 115 (2013), 146-160. 8
27. Poco J., Etemadpour R., Paulovich F.V., Long T., Rosenthal P., Oliveira M., Linsen L., Minghim R.,: A framework for exploring multidimensional data with 3D projections. Comput. Graph. Forum 30, 3 (2011), 1111-1120. 3
28. Roweis S.T., Saul L.K.,: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 5500 (2000), 2323-2326. 2, 4
29. Singh G., Mémoli F., Carlsson G.,: Topological methods for the analysis of high dimensional data sets and 3D object recognition. Proc. Eurographics Symp. on Point-Based Graphics (2007), 91-100. 3
30. Saul L., Roweis S.,: Think globally, fit locally: unsupervised learning of nonlinear manifolds. J. Mach. Learn. Res. 4 (2003), 119-155. 2
31. Tenenbaum J.B., De Silva V., Langford J.C.,: A global geometric framework for nonlinear dimensionality reduction. Science 290, 5500 (2000), 2319-2323. 1, 4
32. Torgerson W.S.,: Multidimensional scaling: I. theory and method. Psychometrika 17, 4 (1952), 401-419. 1, 2, 3
33. Venna J., Peltonen J., Nybo K., Aidos H., Kaski S.,: Information retrieval perspective to nonlinear dimensionality reduction for data visualization. J. Mach. Learn. Res. 11 (2010), 451-490. 2
34. Yuan X., Ren D., Wang Z., Guo C.,: Dimension projection matrix/tree: Interactive subspace visual exploration and analysis of high dimensional data. IEEE Trans. Vis. Comput. Graphics 19, 12 (2013), 2625-2633. 3