Abstract
We revisit the problem of representing a high-dimensional data set by a distance-preserving projection onto a two-dimensional plane. This problem is solved by well-known techniques, such as multidimensional scaling. There, the data is projected onto a flat plane and the Euclidean metric is used for distance calculation. In real topographic maps, however, travel distance (or time) is not determined by (Euclidean) distance alone, but also influenced by map features such as mountains or lakes. We investigate how to utilize landscape features for a distance-preserving projection. A first approach with rectangular cylindrical mountains in the MDS landscape is presented.
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Tschumitschew, K., Klawonn, F., Höppner, F., Kolodyazhniy, V. (2007). Landscape Multidimensional Scaling. In: R. Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds) Advances in Intelligent Data Analysis VII. IDA 2007. Lecture Notes in Computer Science, vol 4723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74825-0_24
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DOI: https://doi.org/10.1007/978-3-540-74825-0_24
Publisher Name: Springer, Berlin, Heidelberg
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