Pursuit-package: Projection Pursuit
In Pursuit: Projection Pursuit
Pursuit R Documentation
Projection Pursuit
Description
Projection pursuit (PP) with 17 methods and grand tour with 3 methods. Being that projection pursuit searches for low-dimensional linear projections in high-dimensional data structures, while grand tour is a technique used to explore multivariate statistical data through animation.
Details
Package: Pursuit
Type: Package
Version: 1.0.4
Date: 2023-08-19
License: GPL(>= 2)
LazyLoad: yes
Author(s)
Paulo Cesar Ossani and Marcelo Angelo Cirillo.
Maintainer: Paulo Cesar Ossani <ossanipc@hotmail.com>
References
ASIMOV, D. The Grand Tour: A Tool for Viewing Multidimensional Data. SIAM Journal of Scientific and Statistical Computing, 6(1), 128-143, 1985.
ASIMOV, D.; BUJA, A. The grand tour via geodesic interpolation of 2-frames. in Visual Data Exploration and Analysis. Symposium on Electronic Imaging Science and Technology, IS&T/SPIE. 1994.
BUJA, A.; ASIMOV, D. Grand tour methods: An outline. Computer Science and Statistics, 17:63-67. 1986.
BUJA, A.; COOK, D.; ASIMOV, D.; HURLEY, C. Computational Methods for High-Dimensional Rotations in Data Visualization, in C. R. Rao, E. J. Wegman & J. L. Solka, eds, "Handbook of Statistics: Data Mining and Visualization", Elsevier/North Holland, http://www.elsevier.com, pp. 391-413. 2005.
COOK, D., LEE, E. K., BUJA, A., WICKHAM, H.. Grand tours, projection pursuit guided tours and manual controls. In Chen, Chunhouh, Hardle, Wolfgang, Unwin, e Antony (Eds.), Handbook of Data Visualization, Springer Handbooks of Computational Statistics, chapter III.2, p. 295-314. Springer, 2008.
COOK, D., BUJA, A., CABRERA, J.. Projection pursuit indexes based on orthonormal function expansions. Journal of Computational and Graphical Statistics, 2(3):225-250, 1993.
COOK, D., BUJA, A., CABRERA, J., HURLEY, C.. Grand tour and projection pursuit, Journal of Computational and Graphical Statistics, 4(3), 155-172, 1995.
COOK, D., SWAYNE, D. F.. Interactive and Dynamic Graphics for Data Analysis: With R and GGobi. Springer. 2007.
ESPEZUA, S., VILLANUEVA, E., MACIEL, C.D., CARVALHO, A.. A projection pursuit framework for supervised dimension reduction of high dimensional small sample datasets. Neurocomputing, 149, 767-776, 2015.
FRIEDMAN, J. H., TUKEY, J. W. A projection pursuit algorithm for exploratory data analysis. IEEE Transaction on Computers, 23(9):881-890, 1974.
HASTIE, T., BUJA, A., TIBSHIRANI, R.: Penalized discriminant analysis. The Annals of Statistics. 23(1), 73-102 . 1995.
HUBER, P. J.. Projection pursuit. Annals of Statistics, 13(2):435-475, 1985.
HURLEY, C.; BUJA, A. Analyzing high-dimensional data with motion graphics, SIAM Journal of Scientific and Statistical Computing, 11 (6), 1193-1211. 1990.
JONES, M. C., SIBSON, R.. What is projection pursuit, (with discussion), Journal of the Royal Statistical Society, Series A 150, 1-36, 1987.
LEE, E., COOK, D., KLINKE, S., LUMLEY, T.. Projection pursuit for exploratory supervised classification. Journal of Computational and Graphical Statistics, 14(4):831-846, 2005.
LEE, E. K., COOK, D.. A projection pursuit index for large p small n data. Statistics and Computing, 20(3):381-392, 2010.
MARTINEZ, W. L., MARTINEZ, A. R.; Computational Statistics Handbook with MATLAB, 2th. ed. New York: Chapman & Hall/CRC, 2007. 794 p.
MARTINEZ, W. L., MARTINEZ, A. R., SOLKA, J.; Exploratory Data Analysis with MATLAB, 2th. ed. New York: Chapman & Hall/CRC, 2010. 499 p.
PENA, D., PRIETO, F.. Cluster identification using projections. Journal of the American Statistical Association, 96(456):1433-1445, 2001.
POSSE, C.. Projection pursuit exploratory data analysis, Computational Statistics and Data Analysis, 29:669-687, 1995a.
POSSE, C.. Tools for two-dimensional exploratory projection pursuit, Journal of Computational and Graphical Statistics, 4:83-100, 1995b
YOUNG, F. W.; RHEINGANS P. Visualizing structure in high-dimensional multivariate data, IBM Journal of Research and Development, 35:97-107, 1991.
YOUNG, F. W.; FALDOWSKI R. A.; McFARLANE M. M. Multivariate statistical visualization, in Handbook of Statistics, Vol 9, C. R. Rao (ed.), The Netherlands: Elsevier Science Publishers, 959-998, 1993.
Pursuit documentation built on Aug. 20, 2023, 1:07 a.m.
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| Pursuit | R Documentation |
Projection Pursuit
Description
Projection pursuit (PP) with 17 methods and grand tour with 3 methods. Being that projection pursuit searches for low-dimensional linear projections in high-dimensional data structures, while grand tour is a technique used to explore multivariate statistical data through animation.
Details
| Package: | Pursuit |
| Type: | Package |
| Version: | 1.0.4 |
| Date: | 2023-08-19 |
| License: | GPL(>= 2) |
| LazyLoad: | yes |
Author(s)
Paulo Cesar Ossani and Marcelo Angelo Cirillo.
Maintainer: Paulo Cesar Ossani <ossanipc@hotmail.com>
References
ASIMOV, D. The Grand Tour: A Tool for Viewing Multidimensional Data. SIAM Journal of Scientific and Statistical Computing, 6(1), 128-143, 1985.
ASIMOV, D.; BUJA, A. The grand tour via geodesic interpolation of 2-frames. in Visual Data Exploration and Analysis. Symposium on Electronic Imaging Science and Technology, IS&T/SPIE. 1994.
BUJA, A.; ASIMOV, D. Grand tour methods: An outline. Computer Science and Statistics, 17:63-67. 1986.
BUJA, A.; COOK, D.; ASIMOV, D.; HURLEY, C. Computational Methods for High-Dimensional Rotations in Data Visualization, in C. R. Rao, E. J. Wegman & J. L. Solka, eds, "Handbook of Statistics: Data Mining and Visualization", Elsevier/North Holland, http://www.elsevier.com, pp. 391-413. 2005.
COOK, D., LEE, E. K., BUJA, A., WICKHAM, H.. Grand tours, projection pursuit guided tours and manual controls. In Chen, Chunhouh, Hardle, Wolfgang, Unwin, e Antony (Eds.), Handbook of Data Visualization, Springer Handbooks of Computational Statistics, chapter III.2, p. 295-314. Springer, 2008.
COOK, D., BUJA, A., CABRERA, J.. Projection pursuit indexes based on orthonormal function expansions. Journal of Computational and Graphical Statistics, 2(3):225-250, 1993.
COOK, D., BUJA, A., CABRERA, J., HURLEY, C.. Grand tour and projection pursuit, Journal of Computational and Graphical Statistics, 4(3), 155-172, 1995.
COOK, D., SWAYNE, D. F.. Interactive and Dynamic Graphics for Data Analysis: With R and GGobi. Springer. 2007.
ESPEZUA, S., VILLANUEVA, E., MACIEL, C.D., CARVALHO, A.. A projection pursuit framework for supervised dimension reduction of high dimensional small sample datasets. Neurocomputing, 149, 767-776, 2015.
FRIEDMAN, J. H., TUKEY, J. W. A projection pursuit algorithm for exploratory data analysis. IEEE Transaction on Computers, 23(9):881-890, 1974.
HASTIE, T., BUJA, A., TIBSHIRANI, R.: Penalized discriminant analysis. The Annals of Statistics. 23(1), 73-102 . 1995.
HUBER, P. J.. Projection pursuit. Annals of Statistics, 13(2):435-475, 1985.
HURLEY, C.; BUJA, A. Analyzing high-dimensional data with motion graphics, SIAM Journal of Scientific and Statistical Computing, 11 (6), 1193-1211. 1990.
JONES, M. C., SIBSON, R.. What is projection pursuit, (with discussion), Journal of the Royal Statistical Society, Series A 150, 1-36, 1987.
LEE, E., COOK, D., KLINKE, S., LUMLEY, T.. Projection pursuit for exploratory supervised classification. Journal of Computational and Graphical Statistics, 14(4):831-846, 2005.
LEE, E. K., COOK, D.. A projection pursuit index for large p small n data. Statistics and Computing, 20(3):381-392, 2010.
MARTINEZ, W. L., MARTINEZ, A. R.; Computational Statistics Handbook with MATLAB, 2th. ed. New York: Chapman & Hall/CRC, 2007. 794 p.
MARTINEZ, W. L., MARTINEZ, A. R., SOLKA, J.; Exploratory Data Analysis with MATLAB, 2th. ed. New York: Chapman & Hall/CRC, 2010. 499 p.
PENA, D., PRIETO, F.. Cluster identification using projections. Journal of the American Statistical Association, 96(456):1433-1445, 2001.
POSSE, C.. Projection pursuit exploratory data analysis, Computational Statistics and Data Analysis, 29:669-687, 1995a.
POSSE, C.. Tools for two-dimensional exploratory projection pursuit, Journal of Computational and Graphical Statistics, 4:83-100, 1995b
YOUNG, F. W.; RHEINGANS P. Visualizing structure in high-dimensional multivariate data, IBM Journal of Research and Development, 35:97-107, 1991.
YOUNG, F. W.; FALDOWSKI R. A.; McFARLANE M. M. Multivariate statistical visualization, in Handbook of Statistics, Vol 9, C. R. Rao (ed.), The Netherlands: Elsevier Science Publishers, 959-998, 1993.
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