Landscape Multidimensional Scaling
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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.

Author: Frank Klawonn, University Of Applied Sciences Braunschweig/Wolfenbüttel



Tags: VideoLectures.Net, Machine Learning, Preprocessing, Lectures, Computer Science, Science

Level: advanced Date: October 20, 2008 Votes: 0 User: Dmytro Shteflyuk  Comments:
 
 

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