Theoretical differences between KPCA and t-SNE?

I (think I) understand the underlying principles of most dimensionality reduction methods (MDS, IsoMap, t-SNE, Spectral Embedding, Diffusion maps, etc...).

Some of the algorithms I use the most are Kernel PCA (with a gaussian kernel) and t-SNE. My question is, do you know some theoretical resons on when to use t-SNE or kernel PCA? Do you know which are their relative strenghts/weaknesses? Is there some known cases where one is better from the other? Do their results have different characteristics (I have noticed that KPCA tends to preserve the density of points, while t-SNE spreads the points with more or less uniform density)? This type of thing...