Abstract
Topographic mapping offers an intuitive interface to inspect large quantities of electronic data. Recently, it has been extended to data described by general dissimilarities rather than Euclidean vectors. Unlike its Euclidean counterpart, the technique has quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, it is infeasible already for medium sized data sets. We introduce two approximation techniques which speed up the complexity to linear time algorithms: the Nyström approximation and patch processing, respectively. We evaluate the techniques on three examples from the biomedical domain.
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Gisbrecht, A., Schleif, FM., Zhu, X., Hammer, B. (2011). Linear Time Heuristics for Topographic Mapping of Dissimilarity Data. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_4
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DOI: https://doi.org/10.1007/978-3-642-23878-9_4
Publisher Name: Springer, Berlin, Heidelberg
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