I have a set of data points in high-dimensional (512D) space that I wish to map to 2D for visualisation. I am interested in observing in 2D the (approximate) relative distances between the data points and their general spatial structure.
Currently I am using multidimensional scaling within Matlab to do this:
% Dissimilarity matrix D = pdist(X', 'euclidean'); % Non-metric MDS -- force dimensionality to 2D Y = mdscale(D, 2)'; % Display result figure, plot(Y(1, :), Y(2, :), 'o');
X is the set of high-dimensional data points (512 x number of points).
Having performed this mapping for a set of points, I wish to be able to project/map new high-dimensional points within the 2D space without needing to use the original set of high-dimensional points.
(1) How do I obtain the mapping/projection used to scale the 512D points to 2D so that I can apply it to new data points?
(2) What would be a suitable "error metric" to evaluate the projections? The purpose of doing this is to compare different dimensionality reduction techniques (e.g. MDS vs LLE).
For example, would a comparison of the distances between respective k nearest neighbours within both spaces be suitable? i.e. sum the squared differences between corresponding neighbour distances within the two spaces for each point and compute the mean.
so that I can apply it to new data points?How do you see it? MDS input is one or several matrices of a dissimilarity within a fixed set of objects. $\endgroup$