i (and hope others as well) would be very interested to know how this compares w.r.t canonical dimensionality reduction mechanisms e.g. svd/pca etc.
to me it seems that it _might_ not be as 'structure' preserving as others.
to me it seems that it _might_ not be as 'structure' preserving as others.
You can also compare them on computational complexity, where random projection (O(numPoints * numOriginalDimensions * numProjectedDimensions) smokes PCA or SVD which are cubic in the number of original dimensions.
And then there's simplicity. The random projection method turns on sampling from a normal distribution and then doing a matrix multiplication. There's a whole lot more about PCA to understand (standardizing your data, calculating the covariance matrix, eigenvector decomposition). I doubt I could implement it correctly myself, and I surely couldn't do it in high dimension.