VI. Conclusion and Discussion of Future Work
6.2 Future Work
My future work will still be evolving around the topic of visual analytics. I am currently developing a more effective tool for our visual algebra and extending the uncertainty model to incorporate the uncertain graph structure. In addition, I im- prove and implement the dimension reduction technique into a existing visualization system, such as R and Cytoscape. There are a number of functions that support user developed packages for both systems. Moreover, I am planing to extend the di- mension reduction method to a data mining method for high dimensional data with network structures. By incorporating the additional network structure, we hope to improve the predication rate for all observations.
This thesis has presented many practical methods that support different aspects of visual analytics, improving our ability to explore data both statistically and visually. The methods are not just computational, but also provide a mental framework that supports a data visual analytic process and stresses a mixture of human and machine
BIBLIOGRAPHY
Anderson, J. R. (1995), Learning and Memory, Wiley and Sons Inc.
Argyriou, A., T. Evgeniou, and M. Pontile (2006), Multi-task feature learning, Jour- nal of Machine Learning, 10, 243–272.
Argyriou, A., C. Micchelli, M. Pontile, and Y. Ying (2008), A spectral regulariza- tion framework for multi-task structure learning, Neural Information Processing Systems.
Auber, D., Y. Chiricota, F. Jourdan, and G. Melanc (2003), Multiscale visualization of small world networks, In Proceedings of the IEEE Symposium on Information Visualization, pp. 75–81.
Bakker, B., and T. Heskes (2003), Task clustering and gating for bayesian multi-task learning, Journal of Machine Learning Research, 4, 83–99.
Batagelj, V., and A. Mrvar (2009), Pajek program for large network analysis, Home page: http://vlado.fmf.uni-lj.si/pub/networks/pajek/.
Baxter, J. (2000), A model of inductive bias learning, Journal of Artificial Intelligence Research, 12, 149–198.
Ben-David, S., and R. Schuller (2003), Exploiting tasks relatedness for multiple task learning, Proceedings of Computational Learning Theory (COLT), 37, 373–384. Borg, I., and P. Groenen (1997), Modern Multidimensional Scaling: theory and ap-
plications, Springer Series in Statistics.
Breiman, L., and J. Friedman (1997), Predicting multivariate responses in multiple linear regression, Journal of the Royal Statistical Society,Series B, 59(1), 3–54. Caponnetto, A., C. Micchelli, M. Pontile, and Y. Ying (2008), Universal multi-task
kernels, Journal of Machine Learning Research, 9, 1615–1646.
Chen, J., J. Liu, and J. Ye (2010), Learning incoherent sparse and low-rank patterns from multiple tasks, KDD.
Choo, J., S. Bohn, and H. Park (2009), Two-stage framework for visualization of clustered high dimensional data, Visual Analytics Science and Technology, pp. 67– 74.
Collaboration, T. C. (2006), Cytoscape Users Manual, Institute for Systems Biology and University of California San Diego.
Costa, L., F. Rodrigues, G. Travieso, and V. Boas (2007), Characterization of complex networks, Advances in Physics, 56 (1), 167–242.
Czanner, G., S. V. Sarma, U. Eden, and E. Brown (2008), A signal-to nosie ratio estimator for generalized linear model systems, World Congress on Engineering. de Leeuw, J., and G. Michailidis (2000), Optimization transfer using surrogate objec-
tive functions, Journal of Computational and Graphical Statistics, 9 (1), 26–31. Elmqvist, N., T. N. Do, H. Goodell, N. Henry, and J. Fekete (2008a), Zame interactive
large-scale graph visualization, In Proceedings of the IEEE Pacific Visualization Symposium 2008, pp. 215–222.
Elmqvist, N., P. Dragicevic, and J.-D. Fekete (2008b), Rolling the dice: Multidimen- sional visual exploration using scatterplot matrix navigation, IEEE Transactions on Visualization and Computer Graphics, 14 (6), 1539–1148.
Evgeniou, T., and M. Pontil (2004), Regularized multi-task learning, KDD’ 04. Gene H. Golub, C. F. V. L. (1996), Matrix computations, third ed., Johns Hopkins
studies in the mathematical sciences.
Grammel, L., M. Tory, and M.-A. Storey (2010), How information visualization novices construct visualizations, IEEE Transactions on Visualization and Com- puter Graphics, 16 (6), 943–952.
Grinstein, G., C. Plaisant, S. Laskowski, T. O’Connell, J. Scholtz, and M. Whiting (2008), Vast 2008 challenge: Introducing mini challenges, Proceedings of IEEE Symposium, 1 (1), 195–196.
Hanrahan, P. (2006), Vizql a language for query, analysis and visualization, SIGMOD. Heer, J., S. K. Card, and J. A. Landay (2005), Prefuse: a toolkit for interactive information visualization, In CHI 2005 Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 421–430.
Henry, N., J.-D. Fekete, and M. J. McGuffin (2007), Nodetrix: a hybrid visualization of social networks, IEEE Transactions on Visualization and Computer Graphics, 13 (6), 1302–1309.
Heskes, T. (2000), Empirical bayes for learning to learn, Proceedings of ICML-2000,ed Langley,P., pp. 367–374.
Holten, D. (2006), Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data, IEEE Transactions on Visualization and Computer Graphics, 12 (5), 741–748.
Hornik, K., and A. Gebhardt (1998), MASS Package, R User Manual, CRAN. Ideker, T., V. Thorsson, J. A. Ranish, et al. (2001), Integrated genomic and proteomic
analyses of a systematically perturbed metabolic network, Science, 292 (4), 929– 934.
I.Kecskes, and T. Papp (2000), Foreign language and mother tongue, Hillsdale. J.D., B. A. Brown, and R. Cocking (1999), How people learn: brain, mind, experience
and school, National Academy Press.
Jolliffe, I. (2002), Principal Component Analysis, second ed., Springer Series in Statis- tics.
Laramee, R., and R. Kosara (2007), Challenges and unsolved problem, In Human- Centered Visualization Environments, pp. 231–254.
Lounici, K., M. Pontile, A. Tsybakov, and S. vande Geer (2009), Taking advantage of sparsity in multi-task learning, Conference on Learning Theory.
Ma, K.-L. (2003), Visualizing time-varying volume data, IEEE Computing in Science and Engineering, 5 (3), 34–42.
McLachlan, G. J. (2004), Discriminant analysis and statistical pattern recognition, Wiley series in probability and mathematical statistics.
Michailidis, G. (2006), Data Visualization Through Their Graph Representations. Miriah Meyer, T. M., and H. Pfister (2010), Multeesum: A tool for comparative
spatial and temporal gene expression data, IEEE Transactions on Visualization and Computer Graphics, 1 (2), 99–108.
Moustafa, W. E., G. M. Namata, A. Deshpande, and L. Getoor (2011), Declara- tive analysis of noisy information networks, in ICDE Workshop on Graph Data Management: Techniques and Applications.
Muelder, C., and K.-L. Ma (2008), Rapid graph layout using space filling curves, IEEE Transactions on Visualization and Computer Graphics, 14 (6), 1301 – 1308. Obozinski, G., B. Taskar, and M. Jordan (2006), Multi-task feature selection, Tech-
nical Report.
Obozinski, G., M. Wainwright, and M. Jordan (2008), Union support recovery in high-dimensional multivariate regression, Technical Report.
Obozinski, G., B. Taskar, and M. Jordan (2009), Joint covariant selection and joint subspace selection for multiple classification problem, Statistics and Computing.
Oesterling, P., G. Scheuermann, S. Teresniak, G. Heyer, S. Koch, T. Ertl, and G. H. Weber (2010), Two-sage framework for a topology-based projection and visual- ization of classified document collections, IEEE Symposium on Visual Analytics Science and Technology, pp. 91–98.
Olga Troyanskaya, M. C., et al. (2001), Missing value estimation methods for dna microarrays, Biostatistics, 17 (6), 520–525.
Olson, J., and G. Olson (1990), The growth of cognitive modeling in human computer interaction since goms, Human computer interaction, 5, 221–265.
Pontile, M., T. Evgeniou, and A. Argyriou (2007), Convex multi-task feature learning, Journal of Machine Learning, 10, 243–272.
Raudenbush, S., and A. Bryk (2002), Hierarchical Linear Models, Saga Publications, Inc.
Schneidewind, J., and H. Ziegler (2006), Challenges in visual data analysis, Proc. Int’l Conf. Information Visualization (IV), pp. 9–16.
Shaverdian, A., H. Zhou, G. Michailidis, and H. Jagadish (2009a), Algebraic visual analysis: The catalano phone call data set case study, VAKD 09.
Shaverdian, A. A., H. Zhou, G. Michailidis, and H. Jagadish (2009b), Algebraic visual analysis: the catalano phone call data set case study, Proc. ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery.
Shojaie, A., and G. Michailidis (2009), Analysis of gene sets based on the underlying regulatory network, Journal of Computational Biology, 6 (3), 407–426.
Stasko, J., C. Grg, Z. Liu, and K. Singhal (2007), Jigsaw: Supporting investiga- tive analysis through interactive visualization, Proc. IEEE Symp. Visual Analytics Science and Technology, pp. 131–138.
Thomas, J., and K. Cook (2005), Illuminating the path: The research and develop- ment agenda for visual analytics, IEEE Computer Society.
Tibshirani, R. (1996), Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), 58, 267–288.
Travers, J., and S. Milgram. (1969), An experimental study of the small world prob- lem, Sociometry, 32 (4), 425–443.
Tulip, D. A. (2004), A huge graph visualization framework, In Graph Drawing Soft- ware, Mathematics and Visualization, pp. 105–126.
Velleman, P. F. (1997), The philosophical past and the digital future of data analysis 375 years of philosophical guidance for software design on the occassion of john w. tukeys 80th birthday.
Viau, C., M. J. McGuffin, Y. Chiricota, and I. Jurisica (2010), The flowvizmenu and parallel scatterplot matrix: Hybrid multidimensional visualizations for network exploration, IEEE Transactions on Visualization and Computer Graphics, 16 (6), 1100–1108.
Weaver, C. (2008), Multidimensional visual analysis using cross-filtered views, pp. 163–170.
WIKIPEDIA (2011), Scoring algorithm.
Witten, D. M., R. Tibshirani, and T. Hastie (2009), A penalized matrix decompo- sition, with applications to sparse principal components and canonical correlation analysis, Biostatistics, 10 (3), 515–534.
Wong, P., H. Foote, G. C. Jr., P. Mackey, and K. Perrine (2006), Graph signatures for visual analytics, IEEE Transactions on Visualization and Computer Graphics, 12 (6), 1399–1413.
Yuan, X., P. Guo, H. Xiao, H. Zhou, and H. Qu (2009), Scattering points in parallel coordinates, IEEE Transactions on Visualization and Computer Graphics, 15 (6), 1001–1008.
Zhang, J., Z. Ghahramani, and Y. Yang (2008), Flexible laten variable models for multi-task learning, Journal of Machine Learning.