CSDL Home IEEE Transactions on Visualization & Computer Graphics 2010 vol.16 Issue No.06 - November/December

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Issue No.06 - November/December (2010 vol.16)

pp: 1281-1290

Fernando V. Paulovich , Universidade de São Paulo (USP)

Claudio T. Silva , University of Utah

Luis G. Nonato , Universidade de São Paulo (USP)

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.207

ABSTRACT

Most multidimensional projection techniques rely on distance (dissimilarity) information between data instances to embed high-dimensional data into a visual space. When data are endowed with Cartesian coordinates, an extra computational effort is necessary to compute the needed distances, making multidimensional projection prohibitive in applications dealing with interactivity and massive data. The novel multidimensional projection technique proposed in this work, called Part-Linear Multidimensional Projection (PLMP), has been tailored to handle multivariate data represented in Cartesian high-dimensional spaces, requiring only distance information between pairs of representative samples. This characteristic renders PLMP faster than previous methods when processing large data sets while still being competitive in terms of precision. Moreover, knowing the range of variation for data instances in the high-dimensional space, we can make PLMP a truly streaming data projection technique, a trait absent in previous methods.

INDEX TERMS

Dimensionality Reduction; Projection Methods; Visual Data Mining; Streaming Technique

CITATION

Fernando V. Paulovich, Claudio T. Silva, Luis G. Nonato, "Two-Phase Mapping for Projecting Massive Data Sets",

*IEEE Transactions on Visualization & Computer Graphics*, vol.16, no. 6, pp. 1281-1290, November/December 2010, doi:10.1109/TVCG.2010.207REFERENCES

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