Information visualization by dimensionality reduction: a review

  • Authors

    • Safa Najim Bangor University, UK
    2014-06-02
    https://doi.org/10.14419/jacst.v3i2.2746

  • Abstract

    Information visualization can be considered a process of transforming similarity relationships between data points to a geometric representation in order to see unseen information. High-dimensionality data sets are one of the main problems of information visualization. Dimensionality Reduction (DR) is therefore a useful strategy to project high-dimensional space onto low-dimensional space, which it can be visualized directly. The application of this technique has several benefits. First, DR can minimize the amount of storage needed by reducing the size of the data sets. Second, it helps to understand the data sets by discarding any irrelevant features, and to focus on the main important features. DR can enable the discovery of rich information, which assists the task of data analysis. Visualization of high-dimensional data sets is widely used in many fields, such as remote sensing imagery, biology, computer vision, and computer graphics. The visualization is a simple way to understand the high-dimensional space because the relationship between original data points is incomprehensible. A large number of DR methods which attempt to minimize the loss of original information. This paper discuss and analys some DR methods to support the idea of dimensionality reduction to get trustworthy visualization.

    Keywords: Dimensionality Reduction, Information visualization, Information retrieval.

  • References

    1. J. Zhang, Visualization For Information Retrieval, Springer-Verlag Berlin Heidelberg, 2008.
    2. I. Borg, P. Groenen, Modern Multidimensional Scaling: Theory and Applications., Springer Verlag, 2005.
    3. S. Nishisato, Multidimensional Nonlinear Descriptive Analysis, Boca Raton, FL: Chapman & Hall, 2006.
    4. J. A. Lee, M. Verleysen, Nonlinear Dimensionality Reduction, Springer, 2007.
    5. L. Yang, Distance-preserving dimensionality reduction., Wiley Interdisc. Rew.: Data Mining And Knowledge Discovery 1 (2011) 369-380.
    6. D. K. Agraotis, Stochastic proximity embedding, Computational Chemistry 24 (2003) 1215-1221.
    7. I. T. Jollie, Principal Component Analysis, Springer Verlag, New York, Inc., 2002.
    8. J. B. Tenenbaum, V. de Silva, J. C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science 290 (2000) 2319-2323.
    9. J. Clark, D. A. Holton, A First Look At Graph Theory, World Scientic Publishing Co. Pte. Ltd., 2005.
    10. O. Samko, A. Marshall, P. Rosin, Selection of the optimal parameter value for the isomap algorithm, Pattern Recognition Letters 27 (2006) 968-979.
    11. X. Zeng, S. Luo, Generalized locally linear embedding based on local reconstruction similarity, in: Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008.
    12. J. A. Lee, A. Lendasse, M. Verleysen, Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis, Neurocomputing 57 (2004) 49-76.
    13. J. X. Li, Visualization of high-dimensional data with relational perspective map, Information Visualization 3 (2004) 49-59.
    14. S. A. Najim, I. S. Lim, Trustworthy dimension reduction for visualization dierent data sets, Information Sciences (2014) http://dx.doi.org/10.1016/j.ins.2014.03.048.
    15. S. Kaski, J. Peltonen, Dimensionality reduction for data visualization, IEEE Signal Processing Magazine 28 (2011) 100-104.
    16. A. Telea, Data Visualization Principles and Practice, A K peters, Ltd, 2008.
    17. T. Schreck, T. von Landesberger, S. Bremma, Techniques for precision-based visual analysis of projected data., in: IS & T/SPIE Conference on Visualization and Data Analysis (VDA 2010) . San Jose, California., 2010.
    18. S. Lespinats, M. Aupetit, CheckViz: Sanity check and topological clues for linear and non-linear mappings, Computer Graphics Forum 30 (2011) 113-125.
    19. S. Kaski, J. Nikkil, M. Oja, J. Venna, P. Trnen, E. Castrn, Trustworthiness and metrics in visualizing similarity of gene expression, BMC Bioinformatics 4 (2003) 1-13.
    20. J. Venna, S. Kaski, Local multidimensional scaling with controlled tradeo between trustworthiness and con- tinuity, in: WSOM Conference 05. Paris, France, 2005.
    21. M. Mignotte, A bicriteria optimization approach based dimensionality reduction model for the color display of hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing 50 (2012) 501-513.
    22. L. Chen, A. Buja, Local multidimensional scaling for nonlinear dimension reduction, graph drawing, and proximity analysis, Journal of the American Statistical Association 104 (2009) 209-219.

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  • How to Cite

    Najim, S. (2014). Information visualization by dimensionality reduction: a review. Journal of Advanced Computer Science & Technology (JACST), 3(2), 101-112. https://doi.org/10.14419/jacst.v3i2.2746

    Received date: 2014-05-07

    Accepted date: 2014-05-31

    Published date: 2014-06-02