Projection inspector: Assessment and synthesis of multidimensional projections

Neurocomputing

Volumn 150, Issue PB, 2015, Pages 599-610

  Pagliosa, Paulo ^a   Paulovich, Fernando V ^b   Minghim, Rosane ^b   Levkowitz, Haim ^c   Nonato, Luis Gustavo ^b  

^a FEDERAL UNIVERSITY OF MATO GROSSO DO SUL   (Brazil)

^b UNIVERSITY OF SÃO PAULO   (Brazil)

^c UNIVERSITY OF MASSACHUSETTS LOWELL   (United States)

Author keywords

Multidimensional projection; Quality metrics

Indexed keywords

VISUALIZATION;

MULTI-DIMENSIONAL DATA VISUALIZATION; MULTIDIMENSIONAL PROJECTION; PROJECTION SPACE; PROJECTION TECHNIQUES; QUALITY METRICS; THEORETICAL FOUNDATIONS; VISUAL PROPERTIES; VISUALIZATION TECHNIQUE;

DATA VISUALIZATION;

ACCURACY; ARTICLE; DATA ANALYSIS; INFORMATION PROCESSING; MATHEMATICAL COMPUTING; MEASUREMENT ERROR; MULTIDIMENSIONAL PROJECTION; PROJECTION INSPECTOR; QUALITY CONTROL; STATISTICAL ANALYSIS;

EID: 84922625489     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.07.072     Document Type: Article

References (46)

1. Cook D., Buja A., Cabrera J., Hurley C. Grand tour and projection pursuit. J. Comput. Graph. Stat. 1995, 4(3):155-172.
2. J. Seo, B. Shneiderman, A rank-by-feature framework for unsupervised multidimensional data exploration using low dimensional projections, in: IEEE Symposium on Information Visualization, IEEE, 2004, pp. 65-72.
3. C. Seifert, V. Sabol, W. Kienreich, Stress maps: analysing local phenomena in dimensionality reduction based visualisations, in: EuroVAST 2010: International Symposium on Visual Analytics Science and Technology, The Eurographics Association, 2010, pp. 13-18.
4. S. Lespinats, M. Aupetit, Checkviz: sanity check and topological clues for linear and non-linear mappings, in: Computer Graphics Forum, vol. 30, 2011, pp. 113-125.
5. Bertini E., Tatu A., Keim D. Quality metrics in high-dimensional data visualization. an overview and systematization. IEEE Trans. Vis. Comput. Graph. 2011, 17(12):2203-2212.
6. I. Icke, A. Rosenberg, Visual and semantic interpretability of projections of high dimensional data for classification tasks, arXiv preprint . http://arxiv:1205.4776.
7. A. Tatu, P. Bak, E. Bertini, D. Keim, J. Schneidewind, Visual quality metrics and human perception: an initial study on 2d projections of large multidimensional data, in: Proceedings of the International Conference on Advanced Visual Interfaces, 2010, pp. 49-56.
8. D. Marghescu, Evaluating the effectiveness of projection techniques in visual data mining, in: Proceedings of the 6th IASTED International Conference on Visualization, Imaging, and Image Processing, 2006, pp. 94-103.
9. Sedlmair M., Tatu A., Munzner T., Tory M. A taxonomy of visual cluster separation factors. Comput. Graph. Forum 2012, 31(3pt4):1335-1344.
10. Asimov D. The grand tour. a tool for viewing multidimensional data. SIAM J. Sci. Stat. Comput. 1985, 6(1):128-143.
11. Dhillon I.S., Modha D.S., Spangler W. Class visualization of high-dimensional data with applications. Comput. Stat. Data Anal. 2002, 41(1):59-90.
12. Elmqvist N., Dragicevic P., Fekete J.-D. Rolling the dice. multidimensional visual exploration using scatterplot matrix navigation. IEEE Trans. Vis. Comput. Graph. 2008, 14(6):1148-1539.
13. Friedman J.H., Tukey J.W. A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput. 1974, 100(9):881-890.
14. Huber P.J. Projection pursuit. Ann. Stat. 1985, 435-475.
15. A. Tatu, G. Albuquerque, M. Eisemann, J. Schneidewind, H. Theisel, M. Magnor, D. Keim, Combining automated analysis and visualization techniques for effective exploration of high-dimensional data, in: IEEE Symposium on Visual Analytics Science and Technology (VAST), 2009.
16. Sips M., Neubert B., Lewis J.P., Hanrahan P. Selecting good views of high-dimensional data using class consistency. Comput. Graph. Forum 2009, 28(3):831-838.
17. Schreck T., von Landesberger T., Bremm S. Techniques for precision-based visual analysis of projected data. Inf. Vis. 2010, 9(3):181-193.
18. D.J. Lehmann, G. Albuquerque, M. Eisemann, M. Magnor, H. Theisel, Selecting coherent and relevant plots in large scatterplot matrices, in: Computer Graphics Forum, vol. 31, 2012, pp. 1895-1908.
19. J. Wang, W. Peng, M.O. Ward, E.A. Rundensteiner, Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets, in: IEEE Symposium on Information Visualization, IEEE, 2003, pp. 105-112.
20. Aupetit M. Visualizing distortions and recovering topology in continuous projection techniques. Neurocomputing 2007, 70(7):1304-1330.
21. N. Heulot, M. Aupetit, J. Fekete, Proxilens: interactive exploration of high-dimensional data using projections, in: Eurovis Workshop on Visual Analytics using Multidimensional Projections, 2013.
22. Daniels I., Anderson E.W., Nonato L.G., Silva C.T., et al. Interactive vector field feature identification. IEEE Trans. Vis. Comput. Graph. 2010, 16(6):1560-1568.
23. Ding S., Hua X. Recursive least squares projection twin support vector machines for nonlinear classification. Neurocomputing 2014, 130:3-9.
24. Gomez-Nieto E., San Roman F., Pagliosa P., Casaca W., Helou E.S., de Oliveira M.C.F., Nonato L.G. Similarity preserving snippet-based visualization of web search results. IEEE Trans. Vis. Comput. Gr. 2014, 20(3):457-470.
25. Floater M.S. Mean value coordinates. Comput. Aided Geom. Des. 2003, 20(1):19-27.
26. Paulovich F.V., Silva C.T., Nonato L.G. Two-phase mapping for projecting massive data sets. IEEE Trans. Vis. Comput. Graph. 2010, 16(6):1281-1290.
27. R. Fergus, P. Perona, A. Zisserman, Object class recognition by unsupervised scale-invariant learning, in: CVPR, vol. 2, 2003, pp. 264-271.
28. J. Yang, Y.-G. Jiang, A.G. Hauptmann, C.-W. Ngo, Evaluating bag-of-visual-words representations in scene classification, in: International Workshop on Multimedia Information Retrieval, 2007, pp. 197-206.
29. Wilkinson L., Wills G. Scagnostics distributions. J. Comput. Graph. Stat. 2008, 17(2):473-491.
30. L. Wilkinson, A. Anand, R. Grossman, Graph-theoretic scagnostics, in: Proceedings of the 2005 IEEE Symposium on Information Visualization, INFOVIS Z05, IEEE Computer Society, Washington, DC, USA, 2005, pp. 21. URL 〈〉. http://dx.doi.org/10.1109/INFOVIS.2005.14.
31. Venna J., Peltonen J., Nybo K., Aidos H., Kaski S. Information retrieval perspective to nonlinear dimensionality reduction for data visualization. J. Mach. Learn. Res. 2010, 11:451-490. URL 〈http://dl.acm.org/citation.cfm?id=1756006.1756019〉.
32. Martins R.M., Coimbra D.B., Minghim R., Telea A. Visual analysis of dimensionality reduction quality for parameterized projections. Comput. Graph. 2014, 41(0):26-42. URL 〈http://dx.doi.org/10.1016/j.cag.2014.01.006〉.
33. Kruskal J.B. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 1964, 29:115-129.
34. Geng X., Zhan D.-C., Zhou Z.-H. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2005, 35(6):1098-1107.
35. Joia P., Coimbra D., Cuminato J.A., Paulovich F.V., Nonato L.G. Local affine multidimensional projection. IEEE Trans. Vis. Comput. Graph. 2011, 17:2563-2571.
36. Kaski S., Nikkilä J., Oja M., Venna J., Törönen P., Castrén E. Trustworthiness and metrics in visualizing similarity of gene expression. BMC Bioinf. 2003, 4(1):48.
37. Hughes T.J. The Finite Element Method. Linear Static and Dynamic Finite Element Analysis 2012, Dover Publications. com.
38. Paulovich F.V., Nonato L.G., Minghim R., Levkowitz H. Least square projection. a fast high-precision multidimensional projection technique and its application to document mapping. IEEE Trans. Vis. Comput. Graph. 2008, 14(3):564-575.
39. E. Pekalska, D. de Ridder, R.P.W. Duin, M.A. Kraaijveld, A new method of generalizing Sammon mapping with application to algorithm speed-up, in: M. Boasson, J.A. Kaandorp, J.F.M. Tonino, M.G. Vosselman (Eds.), Annual Conference of the Advanced School for Computing and Imaging, 1999, pp. 221-228.
40. Morrison A., Chalmers M. A pivot-based routine for improved parent-finding in hybrid MDS. Inf. Vis. 2004, 3(2):109-122.
41. Tejada E., Minghim R., Nonato L.G. On improved projection techniques to support visual exploration of multidimensional data sets. Inf. Vis. 2003, 2(4):218-231.
42. A. Asuncion, D. Newman, UCI Machine Learning Repository (2007). URL 〈〉. http://www.ics.uci.edu/~mlearn/MLRepository.html.
43. Cortez P., Cerdeira A., Almeida F., Matos T., Reis J. Modeling wine preferences by data mining from physicochemical properties. Decis. Support Syst. 2009, 47(4):547-553.
44. A. Artero, M. de Oliveira, H. Levkowitz, Enhanced high dimensional data visualization through dimension reduction and attribute arrangement, in: Tenth International Conference on Information Visualization, IV 2006, 2006, pp. 707-712.
45. Gower J.C., Dijksterhuis G.B. Procrustes Problems 2004, vol. 3. Oxford University Press Oxford.
46. F.J. García-Fernández, M. Verleysen, J.A. Lee, I. Díaz, Stability comparison of dimensionality reduction techniques attending to data and parameter variations, in: International Workshop on Visual Analytics using Multidimensional Projections (VAMP-EuroVis), 2013.