Computer graphics (2nd ed. in C): principles and practice
Computer graphics (2nd ed. in C): principles and practice
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Self-Organizing Maps
MDSpolar: a new approach for dimension reduction to visualize high dimensional data
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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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.