Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections

Computer Graphics Forum

Volumn 34, Issue 3, 2015, Pages 271-280

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

^a UNIVERSITY OF UTAH   (United States)

^b LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

Author keywords

I.3.3 Computer Graphics : Picture Image Generation Line and curve generation

Indexed keywords

COMPUTER GRAPHICS;

ANIMATED TRANSITIONS; HIGH DIMENSIONAL DATA; HIGH DIMENSIONAL DATASETS; HIGH-DIMENSIONAL DATASET; INTRINSIC DIMENSIONS; INTUITIVE EXPLORATION; LINE AND CURVE GENERATION; SUB-SPACE CLUSTERING;

CLUSTERING ALGORITHMS;

EID: 84937830696     PISSN: 01677055     EISSN: 14678659     Source Type: Journal    
DOI: 10.1111/cgf.12639     Document Type: Article

References (38)

1. Asimov D.,: The grand tour: a tool for viewing multidimensional data. SIAM Journal on Scientific and Statistical Computing 6, 1 (1985), 128-143. 2, 8
2. Anand A., Wilkinson L., Dang T.N.,: Visual pattern discovery using random projections. In VAST (2012), IEEE, pp. 43-52. 3
3. Buja A., Cook D., Asimov D., Hurley C.,: Computational methods for high-dimensional rotations in data visualization. Handbook of statistics: Data mining and data visualization 24 (2005), 391-413. 4, 5
4. Belhumeur P.N., Hespanha J.P., Kriegman D.J.,: Eigenfaces vs. fisherfaces: Recognition using class specific linear projections. IEEE Pattern Analysis and Machine Intelligence 19, 7 (1997). 7
5. Cook D., Buja A., Cabrera J., Hurley C.,: Grand tour and projection pursuit. Journal of Computational and Graphical Statistics 4, 3 (1995), 155-172. 2
6. Cheng C.-H., Fu A.W., Zhang Y.,: Entropy-based subspace clustering for mining numerical data. In Proceedings ACM SIGKDD international conference on Knowledge discovery and data mining (1999), pp. 84-93. 2
7. Elmqvist N., Dragicevic P., Fekete J.-D.,: Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE TVCG 14, 6 (2008), 1539-1148. 2, 8
8. Elhamifar E., Vidal R.,: Sparse subspace clustering. In IEEE CVPR (2009). 2
9. Friedman J.H., Tukey J.W.,: A projection pursuit algorithm for exploratory data analysis. 2
10. Fukunaga K.,: Introduction to statistical pattern recognition. Academic Press, 1990. 2
11. Gabriel R.K., Sokal R.R.,: A new statistical approach to geographic variation analysis. Systematic Zoology, 18, 3 (1969), 259-278. 5
12. Harris J.,: Algebraic geometry: a first course, vol. 133. Springer, 1992. 4
13. Hurley C., Oldford R.,: Graphs as navigational infrastructure for high dimensional data spaces. Computational Statistics 26, 4 (2011), 585-612. 2
14. 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. Combustion and Flame 145 (2006), 145-159. 6
15. Izenman A.J.,: Introduction to manifold learning. Wiley Interdisciplinary Reviews: Computational Statistics 4, 5 (2012), 439-446. 1
16. Jeong D.H., Ziemkiewicz C., Fisher B., Ribarsky W., Chang R.,: ipca: An interactive system for pca-based visual analytics. In Computer Graphics Forum (2009), vol. 28, Wiley Online Library, pp. 767-774. 3
17. Kirkpatrick D., Radke J.,: A framework for computational morphology. CG 85 (1985), 217-248. 5
18. Liu G., Lin Z., Yan S., Sun J., Yu Y., Ma Y.,: Robust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1 (2013). 1, 2, 3
19. Liu S., Wang B., Bremer P.-T., Pascucci V.,: Distortion-guided structure-driven interactive exploration of high-dimensional data. Computer Graphics Forum 33, 3 (2014), 101-110. 7
20. Liu S., Wang B., Thiagarajan J.J., Bremer P.-T., Pascucci V.,: Multivariate volume visualization through dynamic projections. In IEEE 4th Symposium on Large Data Analysis and Visualization (2014), IEEE, pp. 35-42. 9
21. Mokbel B., Lueks W., Gisbrecht A., Hammer B.,: Visualizing the quality of dimensionality reduction. Neurocomputing 112 (2013), 109-123. 7
22. Ng A.Y., Jordan M.I., Weiss Y.,: On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems (2001). 2, 3
23. Nam J.E., Mueller K.,: Tripadvisor nd: A tourism-inspired high-dimensional space exploration framework with overview and detail. IEEE TVCG 19, 2 (2013), 291-305. 2, 3, 5, 8, 9
24. 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. Computer Graphics Forum 30, 3 (2011), 1111-1120. 2
25. Parsons L., Haque E., Liu H.,: Subspace clustering for high dimensional data: a review. ACM SIGKDD Explorations Newsletter 6, 1 (2004), 90-105. 2
26. Sun M., North C., Ramakrishnan N.,: A five-level design framework for bicluster visualizations. IEEE TVCG 20, 12 (Dec 2014), 1713-1722. 2
27. Swayne D.F., Temple Lang D., Buja A., Cook D.,: GGobi: evolving from XGobi into an extensible framework for interactive data visualization. Computational Statistics & Data Analysis 43 (2003), 423-444. 2, 4, 5, 8
28. Turkay C., Lundervold A., Lundervold A.J., Hauser H.,: Representative factor generation for the interactive visual analysis of high-dimensional data. IEEE TVCG 18, 12 (2012), 2621-2630. 2
29. Tatu A., Maas F., Farber I., Bertini E., Schreck T., Seidl T., Keim D.,: Subspace search and visualization to make sense of alternative clusterings in high-dimensional data. In VAST (2012), pp. 63-72. 1, 2
30. Tenenbaum J.B., Silva V.D., Langford J.C.,: A global geometric framework for nonlinear dimensionality reduction. Science 290, 5500 (2000), 2319-2323. 1
31. Tatu A., Zhang L., Bertini E., Schreck T., Keim D., Bremm S., von Landesberger T.,: Clustnails: Visual analysis of subspace clusters. Tsinghua Science and Technology 17, 4 (Aug 2012), 419-428. 2
32. Usps handwritten digit dataset. http://statweb.stanford.edu/~tibs/ElemStatLearn/data.html. Accessed: 2014-11-30. 4
33. Vidal R.,: A tutorial on subspace clustering. IEEE Signal Processing Magazine (2011). 1, 3
34. Wilkinson L., Anand A., Grossman R.L.,: Graph-theoretic scagnostics. In InfoVis (2005), vol. 5, p. 21. 1, 2
35. Ye K., Lim L.-H.,: Distance between subspaces of different dimensions. ArXiv e-prints (July 2014). arXiv:1407.0900. 4, 5
36. Yuan X., Ren D., Wang Z., Guo C.,: Dimension projection matrix/tree: Interactive subspace visual exploration and analysis of high dimensional data. IEEE TVCG 19, 12 (2013), 2625-2633. 2
37. Yang J., Ward M.O., Rundensteiner E.A., Huang S.,: Visual hierarchical dimension reduction for exploration of high dimensional datasets. In Proceedings of the Symposium on Data Visualisation 2003 (2003), VISSYM '03, pp. 19-28. 2
38. Zelnik-Manor L., Perona P.,: Self-tuning spectral clustering. In Advances in neural information processing systems (2004), pp. 1601-1608. 3