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Fernando V. Paulovich, Claudio T. Silva, Luis G. Nonato, "TwoPhase Mapping for Projecting Massive Data Sets," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 12811290, November/December, 2010.  
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@article{ 10.1109/TVCG.2010.207, author = {Fernando V. Paulovich and Claudio T. Silva and Luis G. Nonato}, title = {TwoPhase Mapping for Projecting Massive Data Sets}, journal ={IEEE Transactions on Visualization and Computer Graphics}, volume = {16}, number = {6}, issn = {10772626}, year = {2010}, pages = {12811290}, doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.207}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Visualization and Computer Graphics TI  TwoPhase Mapping for Projecting Massive Data Sets IS  6 SN  10772626 SP1281 EP1290 EPD  12811290 A1  Fernando V. Paulovich, A1  Claudio T. Silva, A1  Luis G. Nonato, PY  2010 KW  Dimensionality Reduction; Projection Methods; Visual Data Mining; Streaming Technique VL  16 JA  IEEE Transactions on Visualization and Computer Graphics ER   
[1] D. Achlioptas, Databasefriendly random projections: Johnsonlindenstrauss with binary coins. J. Comput. Syst. Sci., 66 (4): 671–687, 2003.
[2] A. Asuncion and D. Newman, UCI machine learning repository, 2007.
[3] M. Belkin and P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput., 15 (6): 1373–1396, 2003.
[4] U. Brandes and C. Pich, Eigensolver methods for progressive multidimensional scaling of large data. In M. Kaufmann, and D. Wagner editors, Lecture notes in Computer Science, volume 4372, pages 42–53. 2007.
[5] M. M. Bronstein, A. M. Bronstein, R. Kimmel, and I. Yavneh, Multigrid multidimensional scaling. Numerical Linear Algebra with Applications, 13: 149–171, 2006.
[6] O. Bruno, L. G. Nonato, M. Pazoti, and J. Batista, Topological multicontour decomposition for image analysis and image retrieval. Pattern Recognition Letters, 29: 1675–1683, 2008.
[7] M. Chalmers, A linear iteration time layout algorithm for visualizing highdimensional data. In IEEE Visualization, pages 127–ff., 1996.
[8] J. de Leeuw, Applications of convex analysis to multidimensional scaling. Recent Developments in Statistics, pages 133–146, 1977.
[9] V. de Silva, J. Tenenbaum, Sparse multidimensional scaling using landmark points. Technical report, Stanford, 2004.
[10] D. Donoho and C. Grimes, Hessian eigenmaps: Locally linear embedding techniques for highdimensional data. Proc. Natl. Acad. Sci., 100: 5591–5596, 2003.
[11] P. A. Eades, A heuristic for graph drawing. In Congressus Numerantium, volume 42, pages 149–160, 1984.
[12] N. Elmqvist, P. Dragicevic, and J.D. Fekete, Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE Trans. Vis. Comp. Graph., 14 (6): 1141–1148, 2008.
[13] C. Faloutsos and K. Lin, Fastmap: A fast algorithm for indexing, datamining and visualization of traditional and multimedia databases. In ACM SIGMOD, pages 163–174, 1995.
[14] Y. Frishman and A. Tal, Multilevel graph layout on the gpu. IEEE Trans Vis Comput Graph., 13: 1310–1319., 2007.
[15] E. R. Gansner, Y. Koren, and S. North, Graph drawing by stress majorization. In Lecture Notes in Computer Science, volume 3383, pages 239–250. Springer, 2005.
[16] J. Heinrich and D. Weiskopf, Continuous parallel coordinates. IEEE Trans. Vis. Comp. Graph., 15 (6): 1531–1538, 2009.
[17] S. Ingram and T. Munzner, and M. Olano, Glimmer: Multilevel mds on the gpu. IEEE Trans. Vis. Comp. Graph., 15 (2): 249–261, 2009.
[18] I. Jolliffe, Principal Component Analysis. Springer, second edition, 2002.
[19] F. Jourdan and G. Melançon, Multiscale hybrid mds. In Information Visualisation, pages 388–393, 2004.
[20] Y. Koren, L. Carmel, and D. Harel, Ace: A fast multiscale eigenvectors computation for drawing huge graphs. In IEEE Information Visualization, page 137, 2002.
[21] J. B. Kruskal, Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29: 115–129, 1964.
[22] C. D. Meyer, Matrix Analysis and Applied Linear Algebra. SIAM, 2000.
[23] A. Morrison, G. Ross, and M. Chalmers, A hybrid layout algorithm for subquadratic multidimensional scaling. In IEEE Information Visualization, page 152, 2002.
[24] F. V. Paulovich and R. Minghim, HiPP: A novel hierarchical point placement strategy and its application to the exploration of document collections. IEEE Trans. Visual. Comp. Graph., 14 (6): 1229–1236, 2008.
[25] F. V. Paulovich, L. G. Nonato, R. Minghim, and H. Levkowitz, Least square projection: A fast highprecision multidimensional projection technique and its application to document mapping. IEEE Transactions on Visualization and Computer Graphics, 14 (3): 564–575, 2008.
[26] E. Pekalska, D. de Ridder, R. P.W. Duin, and M. A. Kraaijveld, A new method of generalizing Sammon mapping with application to algorithm speedup. In M. Boasson, J. A. Kaandorp, J. F.M. Tonino, and M. G. Vosselman editors, , Annual Conference of the Advanced School for Computing and Imaging, pages 221–228, 1999.
[27] J. Platt, Fastmap, metricmap, and landmark mds are all nyström algorithms. In Intl. Workshop Artificial Intelligence and Statistics, pages 261–268, 2005.
[28] S. T. Roweis and L. K. Saul, Nonlinear dimensionality reduction by locally linear embedding. Science, 290 (5500): 2323–2326, December 2000.
[29] J. W. Sammon, A nonlinear mapping for data structure analysis. In IEEE Transactions on Computers, volume C18, pages 401–409, May 1969.
[30] J. Shewchuk, An introduction to the conjugate gradient method without the agonizing pain. http://www.cs.cmu.edu/quakepaperspainlessconjugategradient.pdf, 1994.
[31] V. D. Silva and J. B. Tenenbaum, Global versus local methods in nonlinear dimensionality reduction. In Advances in Neural Information Processing Systems 15, pages 705–712. MIT Press, 2003.
[32] M. Sips, B. Neubert, J. P. Lewis, and P. Hanrahan, Selecting good views of highdimensional data using class consistency. Computer Graphics Forum, 28 (3): 831–838, 2009.
[33] M. Steinbach, G. Karypis, and V. Kumar, A comparison of document clustering techniques. In Workshop on Text Mining, ACM SIGKDD International Conference on Data Mining, pages 109–110, 2000.
[34] P. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. AddisonWesley, 2005.
[35] E. Tejada, R. Minghim, and L. G. Nonato, On improved projection techniques to support visual exploration of multidimensional data sets. Information Visualization, 2 (4): 218–231, 2003.
[36] J. B. Tenenbaum, V. de Silva, and J. C. Langford, A global geometric framework for nonlinear dimensionality reduction. Science, 290 (5500): 2319–2323, December 2000.
[37] W. S. Torgeson, Multidimensional scaling of similarity. Psychometrika, 30: 379–393, 1965.
[38] D. Whalen and M. L. Norman, Competition data set and description. In 2008 IEEE Visualization Design Contest. http://vis.computer.org/VisWeek2008/viscontests.html, 2008.
[39] M. Williams and T. Munzner, Steerable, progressive multidimensional scaling. In INFOVIS'04, pages 57–64, 2004.