1. A method for mapping large multi-dimensional data sets from an N-dimensional space to an M-dimensional space, wherein N is greater than M, comprising: (a) non-linearly mapping a sample of points from a multi-dimensional data set, each point in the sample of points having an associated set of N-dimensional coordinates, to produce for each point in the sample of points an associated set of M-dimensional training coordinates; (b) selecting a first sub-set of points from the sample of points; (c) determining a first non-linear function for the first sub-set of points that maps the associated set of N-dimensional coordinates for each point in the first sub-set of points to a set of M-dimensional coordinates, wherein the first non-linear function represents a first relationship between the set of N-dimensional coordinates of the first sub-set of points and the associated set of M-dimensional training coordinates; (d) selecting a second sub-set of points from the sample of points; (e) determining a second non-linear function for the second sub-set of points that maps the associated set of N-dimensional coordinates for each point in the second sub-set of points to a set of M-dimensional coordinates, wherein the second non-linear function represents a second relationship between the set of N-dimensional coordinates of the second sub-set of points and the associated set of M-dimensional training coordinates; and (f) mapping additional points from the multi-dimensional data set to the M-dimensional space using at least one of the first and second non-linear functions, thereby generating a set of M-dimensional coordinates for each of the additional points.