Visual to Parametric Interaction (V2PI)

PLoS ONE

Volumn 8, Issue 3, 2013, Pages

  Leman, Scotland C ^a   House, Leanna ^a   Maiti, Dipayan ^a   Endert, Alex ^b   North, Chris ^b  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^b Virginia Polytechnic Institute and State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANALYTIC METHOD; ARTICLE; HUMAN COMPUTER INTERACTION; MATHEMATICAL MODEL; SIMULATION; STATISTICAL PARAMETERS; VISUAL TO PARAMETRIC INTERACTION;

ALGORITHMS; HUMANS; MODELS, THEORETICAL; USER-COMPUTER INTERFACE;

EID: 84875206548     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0050474     Document Type: Article

References (28)

1. Andrews NO, Fox EA, (2007) Recent developments in document clustering. Technical report, Computer Science, Virginia Tech.
2. Jolliffe I (2005) Principal Component Analysis. New York: Springer-Verlag.
3. Kruskal JB, Wish M, (1978) Multidimensional scaling. Sage University Paper series on Quantitative Application in the Social Sciences 48: 07-011.
4. Keim DA, (2002) Information visualization and visual data mining. IEEE Transactions On Visula-tions and Computer Graphics 7: 100-107.
5. Keim D, Mansmann F, Schneidewind J, Thomas J, Ziegler H (2008) Visual analytics: Scope and challenges. In: Simoff S, Bohlen M, Mazeika A, editors, Visual Data Mining: Theory, Techniques and Tools for Visual Analytics, Springer-Verlag, Berlin. pp. 767-90.
6. Icke I, Sklar E (2009) Visual analytics: A multifaceted overview. Technical report, City University of New York.
7. Pike WA, Stasko J, Chang R, O'Connell TA, (2009) The science of interaction. Information Visu-alization 5: 78-99.
8. Jeong DH, Ziemkiewicz C, Fisher B, Ribarsky W, Chang R, (2009) ipca: An interactive system for pca-based visual analytics. Computer Graphics Forum 28: 767-774.
9. Buja A, Swayne DF, Littman ML, Dean N, Hoffman H, (1998) Xgvis: Interactive data visualization with multidimensional scaling. Journal of Computational and Graphical Statistics 5: 78-99.
10. Endert A, Han C, Maiti D, House L, Leman S, et al. (2011) Observation-level interaction with statistical models for visual analytics. In: Visual Analytics Science and Technology (VAST) 2011 IEEE Conference. pp. 121-130.
11. Lederberg J (1989) Excitement and Fascination of Science, Twelve-Step Process for Scientific Experiments: Epicycles of Scientific Discovery. Palo Alto, California: Annual Reviews, Inc.
12. Thomas J, Cook K, (2005) Illuminating the Path. National Visualizations and Analytics Center.
13. Pirolli P, Card S, (2005) Sensemaking processes of intelligence analysts and possible leverage points as identified through cognitive task analysis. Proceddings of the 2005 International Conference on Intelligence Analysis.
14. Card S, Mackinlay J, Shneiderman B (1999) Information Visualization: Using Vision to Think. San Francisco: Morgan Kaufmann.
15. Pak Chung W, Hetzler B, Posse C, Whiting M, Havre S, et al. (2004) In-spire infovis 2004 contest entry. Proceedings of the IEEE Symposium on Information Visualization (INFOVIS'04).
16. Norman DA (1990) The Design of Everyday Things. Doubleday Books, New York.
17. Abowd G, Beale R (1991) Users, systems, and interfaces: Unifying framework for interaction. In: Human Computer Interaction HCI91: People and Computers. Cambridge University Press, Cambridge, volume 6pp. 73-87.
18. Myers B, Hudson SE, Pausch R, (2000) Past, present, and future of user interface software tools. ACM Trans Comput-Hum Interact 7: 3-28.
19. House L, Leman SC, Han C, (2012) Bayesian visual analytics: Bava. Technical report, Department of Statistics, Virginia Tech.
20. Koren Y, Carmel L (2003) Visualization of labeled data using linear transformations. In: Proceedings of the Ninth Annual IEEE Conference on Information Visualization. Washington, DC, USA: IEEE Computer Society, INFOVIS'03pp. 121-128. URL http://dl.acm.org/citation.cfm?id=1947368.1947392.
21. Hastie T, Tibshirani R, Friedman J, (2008) Elements of Statistical Learning: Data mining, Inference and Prediction (2nd Edition). Springer-Verlag.
22. Torgerson WS (1958) Theory and Methods of Scaling. New York: John Wiley.
23. Carroll JD, Chang JJ, (1970) Analysis of individual differences in multidimensional scaling via an n-way generalization of eckart-young decomposition. Psychometrika 35: 238-319.
24. Schiffman SS, Reynolds ML, Young FW (1981) Introduction to Multidimensional Scaling: Theory, Methods, and Applications. New York: Academic Press.
25. Mordecai A (1976) Nonlinear Programming: Analysis and Methods. New Jersey: Prentice-Hall.
26. Tenenbaum JB, de Silva JCL, (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290: 2319-2323.
27. Bishop CM, Svensén M, Williams CKI, (1998) GTM: the generative topographic mapping. Neural Computation 10: 215-234.
28. Tipping ME, Bishop CM, (1999) Mixtures of probabilistic principal component analyzers. Neural Computation 11: 443-482.