Abstract
We present InVis, a tool to visually analyse data by interactively shaping a two dimensional embedding of it. Traditionally, embedding techniques focus on finding one fixed embedding, which emphasizes a single aspects of the data. In contrast, our application enables the user to explore the structures of a dataset by observing and controlling a projection of it. Ultimately it provides a way to search and find an embedding, emphasizing aspects that the user desires to highlight.
Chapter PDF
Similar content being viewed by others
Keywords
- Control Point
- Linear Embedding
- Nonlinear Dimensionality Reduction
- Embedding Technique
- Dimensional Embedding
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Alipanahi, B., Ghodsi, A.: Guided locally linear embedding. Pattern Recognition Letters 32(7), 1029–1035 (2011)
Barshan, E., Ghodsi, A., Azimifar, Z., Zolghadri Jahromi, M.: Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds. Pattern Recognition 44(7), 1357–1371 (2011)
Cutler, A., Breiman, L.: Archetypal analysis. Technometrics 36(4), 338–347 (1994)
Drineas, P., Kannan, R., Mahoney, M.W.: Fast monte carlo algorithms for matrices iii: Computing a compressed approximate matrix decomposition. SIAM Journal on Computing 36(1), 184–206 (2006)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc. (2001)
Lee, D.D., Seung, H.S., et al.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Neumann, M., Garnett, R., Moreno, P., Patricia, N., Kersting, K.: Propagation kernels for partially labeled graphs. In: ICML 2012 Workshop on Mining and Learning with Graphs (MLG 2012), Edinburgh, UK (2012)
Paiva, J.G.S., Schwartz, W.R., Pedrini, H., Minghim, R.: Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data. In: Computer Graphics Forum, vol. 31, pp. 1345–1354. Wiley Online Library (2012)
Paulovich, F.V., Nonato, L.G., Minghim, R., Levkowitz, H.: Least square projection: A fast high-precision multidimensional projection technique and its application to document mapping. IEEE Transactions on Visualization and Computer Graphics 14(3), 564–575 (2008)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Yang, X., Fu, H., Zha, H., Barlow, J.: Semi-supervised nonlinear dimensionality reduction. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 1065–1072. ACM (2006)
Zhang, D., Zhou, Z.H., Chen, S.: Semi-supervised dimensionality reduction. In: Proceedings of the 7th SIAM International Conference on Data Mining, pp. 629–634 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Paurat, D., Gärtner, T. (2013). InVis: A Tool for Interactive Visual Data Analysis. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science(), vol 8190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40994-3_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-40994-3_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40993-6
Online ISBN: 978-3-642-40994-3
eBook Packages: Computer ScienceComputer Science (R0)