Jessica Zeitz Self
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Abstract
Exploring high-dimensional data is challenging. Dimension reduction algorithms, such as weighted multidimensional scaling, support data exploration by projecting datasets to two dimensions for visualization. These projections can be explored through parametric interaction, tweaking underlying parameterizations, and observation-level interaction, directly interacting with the points within the projection. In this article, we present the results of a controlled usability study determining the differences, advantages, and drawbacks among parametric interaction, observation-level interaction, and their combination. The study assesses both interaction technique effects on domain-specific high-dimensional data analyses performed by non-experts of statistical algorithms. This study is performed using Andromeda, a tool that enables both parametric and observation-level interaction to provide in-depth data exploration. The results indicate that the two forms of interaction serve different, but complementary, purposes in gaining insight through steerable dimension reduction algorithms.
- Jamal Alsakran, Yang Chen, Ye Zhao, Jing Yang, and Dongning Luo. 2011. STREAMIT: Dynamic visualization and interactive exploration of text streams. In Proceedings of the 2011 IEEE Pacific Visualization Symposium. IEEE, 131--138. Google Scholar
Digital Library
- R. Amar, J. Eagan, and J. Stasko. 2005. Low-level components of analytic activity in information visualization. In Proceedings of the IEEE Symposium on Information Visualization (INFOVIS’05). 111--117. Google ScholarDigital Library
- Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014. Power to the people: The role of humans in interactive machine learning. AI Mag. 35, 4 (2014), 105--120. Google ScholarDigital Library
- L. Bradel, C. North, L. House, and S. Leman. 2014. Multi-model semantic interaction for text analytics. In Proceedings of the 2014 IEEE Conference on Visual Analytics Science and Technology (VAST’14). 163--172. Google ScholarCross Ref
- Matthew Brehmer, Michael Sedlmair, Stephen Ingram, and Tamara Munzner. 2014. Visualizing dimensionally-reduced data: Interviews with analysts and a characterization of task sequences. In Proceedings of the 5th Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization. ACM, 1--8. Google ScholarDigital Library
- E. T. Brown, J. Liu, C. E. Brodley, and R. Chang. 2012. Dis-function: Learning distance functions interactively. In Proceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST’12). 83--92. Google ScholarDigital Library
- M. Cakmak, C. Chao, and A. L. Thomaz. 2010. Designing interactions for robot active learners. IEEE Trans. Auton. Mental Dev. 2, 2 (Jun. 2010), 108--118. Google ScholarDigital Library
- Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman. 1999. Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann. Google ScholarDigital Library
- Xin Chen, Leanna House, Jessica Zeitz Self, Scotland Leman, Jane Robertson Evia, James Thomas Fry, and Chris North. 2016. Be the data: An embodied experience for data analytics. In Proceedings of the 2016 Annual Meeting of the American Educational Research Association (AERA’16). 20.Google Scholar
- J. Choo, H. Lee, J. Kihm, and H. Park. 2010. iVisClassifier: An interactive visual analytics system for classification based on supervised dimension reduction. In Proceedings of the 2010 IEEE Symposium on Visual Analytics Science and Technology. 27--34. Google ScholarCross Ref
- Kristin A. Cook and James J. Thomas. 2005. Illuminating the Path: The Research and Development Agenda for Visual Analytics. Technical Report. Pacific Northwest National Laboratory, Richland, WA.Google Scholar
- E. P. dos Santos Amorim, E. V. Brazil, J. Daniels, P. Joia, L. G. Nonato, and M. C. Sousa. 2012. iLAMP: Exploring high-dimensional spacing through backward multidimensional projection. In Proceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST’12). 53--62. Google ScholarDigital Library
- Alex Endert, Patrick Fiaux, and Chris North. 2012a. Semantic interaction for sensemaking: Inferring analytical reasoning for model steering. IEEE Trans. Vis. Comput. Graph. 18, 12 (2012), 2879--2888. Google ScholarDigital Library
- Alex Endert, Patrick Fiaux, and Chris North. 2012b. Semantic interaction for visual text analytics. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 473--482. Google ScholarDigital Library
- Alex Endert, Chao Han, Dipayan Maiti, Leanna House, Scotland Leman, and Chris North. 2011. Observation-level interaction with statistical models for visual analytics. In Proceedings of the 2011 IEEE Conference on Visual Analytics Science and Technology (VAST’11). 121--130. Google ScholarCross Ref
- Jerry Alan Fails and Dan R. Olsen, Jr.2003. Interactive machine learning. In Proceedings of the 8th International Conference on Intelligent User Interfaces (IUI’03). ACM, New York, NY, 39--45. Google ScholarDigital Library
- Eric D. Feigelson and G. Jogesh Babu. 2012. Modern Statistical Methods for Astronomy: With R Applications. Cambridge University Press. Google ScholarCross Ref
- James Fogarty, Desney Tan, Ashish Kapoor, and Simon Winder. 2008. CueFlik: Interactive concept learning in image search. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’08). ACM, New York, NY, 29--38. Google ScholarDigital Library
- Stephen L. France and J. Douglas Carroll. 2011. Two-way multidimensional scaling: A review. IEEE Trans. Syst. Man. Cybernet. C 41, 5 (2011), 644--661. Google ScholarDigital Library
- Keinosuke Fukunaga. 2013. Introduction to Statistical Pattern Recognition. Academic Press.Google Scholar
- D. J. Gilmore and T. R. G. Green. 1984. Comprehension and recall of miniature programs. Int. J. Man-Mach. Stud. 21, 1 (1984), 31--48. Google ScholarDigital Library
- Michael Gleicher. 2013. Explainers: Expert explorations with crafted projections. IEEE Trans. Vis. Comput. Graph. 19, 12 (2013), 2042--2051. Google ScholarDigital Library
- Isabelle Guyon and André Elisseeff. 2003. An introduction to variable and feature selection. J. Mach. Learn. Res. 3 (Mar.2003), 1157--1182. Google ScholarDigital Library
- Leanna House and Chao Han. 2015. Bayesian visual analytics: BaVA. Stat. Anal. Data Min. 8, 1 (2015), 1--13. Google ScholarDigital Library
- Xinran Hu, Lauren Bradel, Dipayan Maiti, Leanna House, and Chris North. 2013. Semantics of directly manipulating spatializations. IEEE Trans. Vis. Comput. Graph. 19, 12 (2013), 2052--2059. Google ScholarDigital Library
- S. Ingram, T. Munzner, V. Irvine, M. Tory, S. Bergner, and T. MÃűller. 2010. DimStiller: Workflows for dimensional analysis and reduction. In Proceedings of the 2010 IEEE Symposium on Visual Analytics Science and Technology. 3--10. Google ScholarCross Ref
- Anil K. Jain, Robert P. W. Duin, and Jianchang Mao. 2000. Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1 (2000), 4--37. Google ScholarDigital Library
- Dong Hyun Jeong, Caroline Ziemkiewicz, Brian Fisher, William Ribarsky, and Remco Chang. 2009. iPCA: An interactive system for PCA-based visual analytics. In Computer Graphics Forum, Vol. 28. Wiley Online Library, 767--774. Google ScholarDigital Library
- Sara Johansson and Jimmy Johansson. 2009. Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE Trans. Vis. Comput. Graph. 15, 6 (2009), 993--1000. Google ScholarDigital Library
- Paulo Joia, Danilo Coimbra, Jose A. Cuminato, Fernando V. Paulovich, and Luis G. Nonato. 2011. Local affine multidimensional projection. IEEE Trans. Vis. Comput. Graph. 17, 12 (2011), 2563--2571. Google ScholarDigital Library
- Ian Jolliffe. 2002. Principal Component Analysis. Wiley Online Library.Google Scholar
- Eser Kandogan. 2000. Star coordinates: A multi-dimensional visualization technique with uniform treatment of dimensions. In Proceedings of the IEEE Information Visualization Symposium, Vol. 650. 22.Google Scholar
- E. Kandogan. 2012. Just-in-time annotation of clusters, outliers, and trends in point-based data visualizations. In Proceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST’12). 73--82. Google ScholarDigital Library
- Tasneem Kaochar, Raquel Peralta, Clayton Morrison, Ian Fasel, Thomas Walsh, and Paul Cohen. 2011. Towards understanding how humans teach robots. In International Conference on User Modeling, Adaptation and Personalization. 347--352. Google ScholarDigital Library
- Ashish Kapoor, Bongshin Lee, Desney Tan, and Eric Horvitz. 2010. Interactive optimization for steering machine classification. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’10). ACM, New York, NY, 1343--1352. Google ScholarDigital Library
- Joseph B. Kruskal and Myron Wish. 1978. Multidimensional Scaling. Vol. 11. Sage. Google ScholarCross Ref
- Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. 2015. Principles of explanatory debugging to personalize interactive machine learning. In Proceedings of the 20th International Conference on Intelligent User Interfaces (IUI’15). ACM, New York, NY, 126--137. Google ScholarDigital Library
- Todd Kulesza, Simone Stumpf, Weng-Keen Wong, Margaret M. Burnett, Stephen Perona, Andrew Ko, and Ian Oberst. 2011. Why-oriented end-user debugging of naïve bayes text classification. ACM Trans. Interact. Intell. Syst. 1, 1, Article 2 (Oct. 2011), 31 pages. Google ScholarDigital Library
- Heidi Lam, Enrico Bertini, Petra Isenberg, Catherine Plaisant, and Sheelagh Carpendale. 2012. Empirical studies in information visualization: Seven scenarios. IEEE Transactions on Visualization and Computer Graphics 18, 9 (2012), 1520--1536. Google ScholarDigital Library
- Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling, and Jens Weidmann. 2009. Animals with Attributes: A Dataset for Attribute Based Classification.Google Scholar
- Scotland C. Leman, Leanna House, Dipayan Maiti, Alex Endert, and Chris North. 2013. Visual to parametric interaction (V2PI). PloS One 8, 3 (2013), e50474.Google ScholarCross Ref
- Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9 (Nov.2008), 2579--2605.Google Scholar
- Kantilal Varichand Mardia, John T. Kent, and John M. Bibby. 1980. Multivariate analysis. (1980).Google Scholar
- Tamara Munzner. 2014. Visualization Analysis and Design. CRC Press, Boca Raton, FL.Google Scholar
- Jakob Nielsen. 1993. Iterative user-interface design. Computer 26, 11 (1993), 32--41. Google ScholarDigital Library
- Chris North. 2006. Toward measuring visualization insight. IEEE Comput. Graph. Appl. 26, 3 (May 2006), 6--9. Google ScholarDigital Library
- Paulo Pagliosa, Fernando V Paulovich, Rosane Minghim, Haim Levkowitz, and Luis Gustavo Nonato. 2015. Projection inspector: Assessment and synthesis of multidimensional projections. Neurocomputing 150 (2015), 599--610. Google ScholarCross Ref
- Fernando V. Paulovich, Cláudio T. Silva, and Luis Gustavo Nonato. 2012. User-centered multidimensional projection techniques. Comput. Sci. Eng. 14, 4 (2012), 74--81. Google ScholarDigital Library
- Daniel Pérez, Leishi Zhang, Matthias Schaefer, Tobias Schreck, Daniel Keim, and Ignacio Díaz. 2015. Interactive feature space extension for multidimensional data projection. Neurocomputing 150 (2015), 611--626.Google ScholarCross Ref
- PNNL. 2010. IN-SPIRE Visual Document Analysis. (2010).Google Scholar
- Brian D. Ripley. 2007. Pattern Recognition and Neural Networks. Cambridge University Press. Google ScholarDigital Library
- Purvi Saraiya, Chris North, and Karen Duca. 2005. An insight-based methodology for evaluating bioinformatics visualizations. IEEE Trans. Vis. Comput. Graph. 11, 4 (2005), 443--456. Google ScholarDigital Library
- Matthias Schaefer, Leishi Zhang, Tobias Schreck, Andrada Tatu, John A. Lee, Michel Verleysen, and Daniel A. Keim. 2013. Improving projection-based data analysis by feature space transformations. In IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 86540H--86540H. Google ScholarCross Ref
- Jessica Zeitz Self, Nathan Self, Leanna House, Jane Robertson Evia, Scotland Leman, and Chris North. 2017. Bringing interactive visual analytics to the classroom for developing EDA skills. In Proceedings of the Consortium for Computing Sciences in Colleges, Eastern Region (CCSC-ER). 10.Google Scholar
- Jessica Zeitz Self, R. K. Vinayagam, James Thomas Fry, and Chris North. 2016. Bridging the gap between user intention and model parameters for data analytics. In Proceedings of the SIGMOD 2016 Workshop on Human-In-the-Loop Data Analytics (HILDA’16). 6. Google ScholarDigital Library
- Jinwook Seo and Ben Shneiderman. 2006. Knowledge discovery in high-dimensional data: Case studies and a user survey for the rank-by-feature framework. IEEE Trans. Vis. Comput. Graph. 12, 3 (2006), 311--322. Google ScholarDigital Library
- Ben Shneiderman. 1994. Dynamic queries for visual information seeking. IEEE Softw. 11, 6 (Nov. 1994), 70--77. Google ScholarDigital Library
- Ben Shneiderman. 2010. Designing the User Interface: Strategies for Effective Human-Computer Interaction. Pearson Education India.Google Scholar
- Simone Stumpf, Vidya Rajaram, Lida Li, Weng-Keen Wong, Margaret Burnett, Thomas Dietterich, Erin Sullivan, and Jonathan Herlocker. 2009. Interacting meaningfully with machine learning systems: Three experiments. Int. J. Hum.-Comput. Stud. 67, 8 (2009), 639--662. Google ScholarDigital Library
- Justin Talbot, Bongshin Lee, Ashish Kapoor, and Desney S. Tan. 2009. Ensemblematrix: Interactive visualization to support machine learning with multiple classifiers. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’09). ACM, New York, NY, 1283--1292. Google ScholarDigital Library
- Joshua B. Tenenbaum, Vin De Silva, and John C. Langford. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290, 5500 (2000), 2319--2323. Google ScholarCross Ref
- Warren S. Torgerson. 1958. Theory and methods of scaling. (1958).Google Scholar
- Cagatay Turkay, Arvid Lundervold, Astri Johansen Lundervold, and Helwig Hauser. 2012. Representative factor generation for the interactive visual analysis of high-dimensional data. IEEE Trans. Vis. Comput. Graph. 18, 12 (2012), 2621--2630. Google ScholarDigital Library
- Iris Vessey. 1991. Cognitive fit: A theory-based analysis of the graphs versus tables literature*. Dec. Sci. 22, 2 (1991), 219--240. Google ScholarCross Ref
- Michael J. Way, Jeffrey D. Scargle, Kamal M. Ali, and Ashok N. Srivastava. 2012. Advances in Machine Learning and Data Mining for Astronomy. CRC Press. Google ScholarDigital Library
- J. Wenskovitch, I. Crandell, N. Ramakrishnan, L. House, S. Leman, and C. North. 2018. Towards a systematic combination of dimension reduction and clustering in visual analytics. IEEE Trans. Vis. Comput. Graph. 24, 1 (Jan. 2018), 131--141. Google ScholarCross Ref
- John Wenskovitch and Chris North. 2017. Observation-level interaction with clustering and dimension reduction algorithms. In Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics (HILDA’17). ACM, New York, NY, Article 14, 6 pages. Google ScholarDigital Library
- Hadley Wickham, Dianne Cook, Heike Hofmann, Andreas Buja, and others. 2011. Tourr: An R package for exploring multivariate data with projections. J. Stat. Softw. 40, 2 (2011), 1--18. Google ScholarCross Ref
- Ji Soo Yi, Youn ah Kang, John Stasko, and Julie Jacko. 2007. Toward a deeper understanding of the role of interaction in information visualization. IEEE Trans. Vis. Comput. Graph. 13, 6 (2007), 1224--1231. Google ScholarDigital Library
- Ji Soo Yi, Rachel Melton, John Stasko, and Julie A. Jacko. 2005. Dust 8 magnet: Multivariate information visualization using a magnet metaphor. Inf. Vis. 4, 4 (2005), 239--256. Google ScholarDigital Library
Index Terms
- Observation-Level and Parametric Interaction for High-Dimensional Data Analysis
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