iLAMP: Exploring high-dimensional spacing through backward multidimensional projection

IEEE Conference on Visual Analytics Science and Technology 2012, VAST 2012 - Proceedings

Volumn , Issue , 2012, Pages 53-62

  Dos Santos Amorim, Elisa Portes ^a   Brazil, Emilio Vital ^a   Daniels II, Joel ^b   Joia, Paulo ^c   Nonato, Luis Gustavo ^c   Sousa, Mario Costa ^a  

^a UNIVERSITY OF CALGARY   (Canada)

^b POLYTECHNIC UNIVERSITY   (United States)

^c UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTING POWER; DATA COLLECTION; DATA COMPLEXITY; DATA SETS; EXPLORATION TECHNIQUES; FLEXIBLE MECHANISMS; HIGH DIMENSIONAL SPACES; HIGH-DIMENSIONAL; INTERACTIVE EXPLORATION; MULTI-DIMENSIONAL DATASETS; MULTIDIMENSIONAL DATA; PLANAR DOMAIN; PROJECTION METHOD; RESEARCH FIELDS; SCIENTIFIC OBSERVATIONS; TECHNOLOGICAL ADVANCES;

GRAPHICAL USER INTERFACES;

EXTRAPOLATION;

EID: 84872945545     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/VAST.2012.6400489     Document Type: Conference Paper

References (51)

1. E. Anderson, Z. Bai, J. Dongarra, A. Greenbaum, A. McKenney, J. Du Croz, S. Hammerling, J. Demmel, C. Bischof, and D. Sorensen. Lapack: a portable linear algebra library for high-performance computers. In Proceedings of the 1990 ACM/IEEE conference on Supercomputing, Supercomputing '90, pages 2-11, 1990.
2. M. Aupetit. Visualizing distortions and recovering topology in continuous projection techniques. Neurocomputing, 70(7-9):1304-1330, 2007.
3. M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computers, 15(6):1373-1396, 2003.
4. M. Berthold and L. O. Hall. Visualizing fuzzy points in parallel coordinates. IEEE Transactions on Fuzzy Systems, pages 369-374, 2003.
5. U. Brandes and C. Pich. Eigensolver methods for progressive multi-dimensional scaling of large data. LNCS, 4372:42-53, 2007.
6. M. Chalmers. A linear iteration time layout algorithm for visualising high-dimensional data. Proceedings of IEEE Conference on Visualization, pages 127-ff, 1996.
7. Y. Chen, L.Wang, M. Dong, and J. Hua. Exemplar-based visualization of large document corpus. IEEE Transactions on Visualization and Computer Graphics, 15:1161-1168, 2009.
8. W. Clevelandand and M. McGill. Dynamic Graphics for Statistics. Wadsworth & Brooks/Cole, 1988.
9. T. Cox and M. Cox. Multidimensional Scaling. Chapman and Hall/CRC, 2nd edition edition, 2000.
10. V. de Silva and J. B. Tenenbaum. Sparse multidimensional scaling using landmark points. Technical report, Stanford, 2004.
11. P. Demartines and J. Herault. Curvilinear component analysis: A self-organizing neural network for nonlinear mapping of data sets. IEEE Transactions on Neural Networks, 8(1):148-154, 1997.
12. P. Eades. A heuristic for graph drawing. Congressus Numerantium, 42:149-160, 1984.
13. N. Elmqvist, P. Dragicevic, and J.-D. Fekete. Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis 2008), 14(6):1141-1148, 2008.
14. M. Ferreira de Oliveira and H. Levkowitz. From visual data exploration to visual data mining: a survey. Visualization and Computer Graphics, IEEE Transactions on, 9(3):378-394, july-sept. 2003.
15. Y. Frishman and A. Tal. Multi-level graph layout on the gpu. IEEE Transactions on Visualization and Computer Graphics, 13:1310-1319, 2007.
16. Y.-H. Fua, M. O.Ward, and E. A. Rundensteiner. Hierarchical parallel coordinates for exploration of large datasets. In IEEE Visualization, 1999.
17. S. Gerber, P.-T. Bremer, V. Pascucci, and R. Whitaker. Visual exploration of high dimensional scalar functions. IEEE Transactions on Visualization and Computer Graphics, 16(6):1271-1280, Nov. 2010.
18. Y. Hajizadeh, E. Amorim, and M. Costa Sousa. Building trust in history matching: the role of multidimensional projection. page to appear, 2012.
19. W. Harvey and Y. Wang. Generating and exploring a collection of topological landscapes for visualization of scalar-valued functions. Proc. Symposium on Visualization, 29, 2010.
20. J. D. II, E. Anderson, L. Nonato, and C. Silva. Interactive vector field feature identification. IEEE Transactions on Visualization and Computer Graphics, 16:1560-1568, 2010.
21. S. Ingram, T. Munzner, and M. Olano. Glimmer: Multilevel mds on the gpu. IEEE Transactions on Visualization and Computer Graphics, 15(2):249-261, 2009.
22. A. Inselberg and B. Dimsdale. Parallel coordinates: a tool for visualizing multi-dimensional geometry. In Proceedings of the 1st conference on Visualization '90, VIS '90, pages 361-378. IEEE Computer Society Press, 1990.
23. H. Janicke, M. Bottinger, and G. Scheuermann. Brushing of attribute clouds for the visualization of multivariate data. IEEE Transactions on Visualization and Computer Graphics, 14(6):1459-1466, 2008.
24. J. Johansson, P. Ljung, M. Jern, and M. Cooper. Revealing structure within clustered parallel coordinates displays. In IEEE Information Visualization, pages 125-132, 2005.
25. P. Joia, D. Coimbra, J. A. Cuminato, F. V. Paulovich, and L. G. Nonato. Local affine multidimensional projection. IEEE Trans. on Vis. Comp. Graph., 17(12):2563-2571, 2011.
26. I. Jolliffee. Principal Component Analysis. Springer-Verlag, 3rd edition edition, 2002.
27. F. Jourdan and G. Melancon. Multiscale hybrid mds. In Proceedings of the Information Visualisation, Eighth International Conference, pages 388-393, Washington, DC, USA, 2004. IEEE Computer Society.
28. E. Kandogan. Star coordinates: A multi-dimensional visualization technique with uniform treatment of dimensions. In Proceedings of the IEEE Information Visualization Symposium, Late Breaking Hot Topics, pages 9-12, 2000.
29. E. Kandogan. Visualizing multi-dimensional clusters, trends, and outliers using star coordinates. In ACM Conference on Knowledge Discovery and Data Mining, pages 107-116, 2001.
30. Y. Koren, L. Carmel, and D. Harel. Ace: A fast multiscale eigenvectors computation for drawing huge graphs. IEEE Information Visualization, pages 137-144, 2002.
31. S. Lespinats and M. Aupetit. False neighbourhoods and tears are the main mapping defaults. How to avoid it? How to exhibit remaining ones? Quality issues, measures of interestingness and evaluation of data mining models, QIMIE, pages 55-65, 2009.
32. A. Morrison, G. Ross, and M. Chalmers. A hybrid layout algorithm for sub-quadratic multidimensional scaling. In IEEE Symposium on Information Visualization, page 152, 2002.
33. A. Morrison, G. Ross, and M. Chalmers. Fast multidimensional scaling through sampling, springs and interpolation. Information Visualization, 2(1):68-77, 2003.
34. M. Novotny and H. Hauser. Outlier-preserving focus+context visualization in parallel coordinates. IEEE Transactions on Visualization and Computer Graphics, 12(5):893-900, 2006.
35. F. Paulovich, D. Eler, J. Poco, C. Botha, R. Minghim, and L. Nonato. Piecewise laplacian-based projection for interactive data exploration and organization. Computer Graphics Forum, 30(3):1091-1100, 2011.
36. F. Paulovich and R. Minghim. Text map explorer: A tool to create and explore document maps. Information Visualization, pages 245-251, 2006.
37. F. Paulovich, L. Nonato, and R. Minghim. Visual mapping of text collections through a fast high precision projection technique. International Conference on Information Visualization, pages 282-290, 2006.
38. F. Paulovich, L. Nonato, R. Minghim, and H. Levkowitz. Least square projection: A fast high-precision multidimensional projection technique and its application to document mapping. IEEE Transactions on Visualization and Computer Graphics, 14(3):564-575, 2008.
39. F. Paulovich, C. Silva, and L. Nonato. Two-phase mapping for projecting massive data sets. IEEE Trans. on Vis. Comp. Graph., 16(6):1281-1290, 2010.
40. W. Peng, M. O. Ward, and E. A. Rundensteiner. Clutter reduction in multi-dimensional data visualization using dimension reordering. IEE Symposium on Information Visualization, pages 89-96, 2004.
41. S. Roweis and L. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, 2000.
42. J. Sammon. A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, 13:401-409, 1964.
43. T. Schreck, M. Schubler, K. Worm, and F. Zeilfelder. Butterfly plots for visual analysis of large point cloud data. pages 33-40, 2008.
44. T. Schreck, T. von Landesberger, and S. Bremm. Techniques for precision-based visual analysis of projected data. Information Visualization, 9:181-193, June 2010.
45. C. Stolte, D. Tang, and P. Hanrahan. Polaris: A system for query, analysis, and visualization of multidimensional relational databases. IEEE Transactions on Visualization and Computer Graphics, 8(1):52-65, 2002.
46. D. Swayne, D. Lang, A. Buja, and D. Cook. Ggobi: evolving from xgobi into an extensible framework for interactive data visualization. Computational Statistics & Data Analysis, 43(4):423-444, 2003.
47. E. Tejada, R. Minghim, and L. Nonato. On improved projection techniques to support visual exploration of multidimensional data sets. Information Visualization, 2(4):218-231, 2003.
48. J. Tenenbaum, V. de Silva, and J. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323, 2000.
49. W. Torgeson. Multidimensional scaling of similarity. Psychometrika, 30:379-393, 1965.
50. J. Wang, W. Peng, M. Ward, and E. Rundensteiner. Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets. IEEE Symposium on Information Visualization, pages 105-112, 2003.
51. L. Yang. Sammon's nonlinear mapping using geodesic distances. In 17th International Conference on Pattern Recognition, volume 2, pages 303-306, 2004.