Representing a point in an n-dimensional space in a 2D geometrical shape

  1. Is it possible for me to use a Voronoi diagram - or some other algorithm - to represent a point in an multidimensional space (between 5 and 10 dimensions) in a 2D geometrical shape? (And more interestingly, in a geometrical shape that is a) fairly small, and b) looks aesthetic.

    I have a number of measurements in standard deviation units on different normal distributions. Say I have 8 standard deviation measurements on 8 different variables. This gives me the following numbers (one for each normal distribution):

    1: -0.3
    2: 1.2
    3: 0.7
    4: 2.1
    5: 0.2
    6: -1.3
    7: -0.2
    8: 1.9

    Can I represent these 8 SD measures as a 2D geometrical shape (to encode them) in such a way that the 2D shapes distinguish between e.g. 10ths of a standard deviation unit (to a level of resolution as in this list)? What are my options here?

    Thanks... Alan
  2. jcsd
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