When to use the Johnson-Lindenstrauss lemma over SVD? The Johnson-Lindenstrauss lemma allows one to represent points in a high dimensional space into points in lower dimension. When finding lower dimensional spaces of best fit, a standard technique is to find the singular value decomposition and then take the subspace generated by the largest singular values. When is it of interest to use Johnson-Lindenstrauss over the SVD?
The two approaches provide very different guarantees. 

The JL Lemma says essentially "you give me the error you want, and I'll give you a low dimensional space that captures the distances upto that error". It's also a worst-case pairwise guarantee: for each pair of points, etc etc

The SVD essentially promises "you tell me what dimension you want to live in, and I'll give you the best possible embedding", where "best" is defined as on average: the total error of true similarity versus projected similarity is minimum. 

So from a theoretical perspective they solve very different problems. In practice, which one you want depends on your model for the problem, what parameters are more important (error or dimension), and what kind of guarantees you need.