Empirical Mode Decomposition and Sparsity In what sense does empirical mode decomposition (EMD) bring out the sparsity in a signal?

For instance, if I had a signal $f$ and I broke it down into $n$ intrinsic mode functions (IMF), what should I do to get a representation that has very few nonzero components? 

Generally when speaking of sparsity, we say that a signal is sparse in some basis. Since IMFs do not have a fixed basis, in what sense can they be (if at all) sparse?
The idea of being sparse in a certain basis can be extended and one can talk about being sparse in a frame, or even, being sparse in a dictionary. Now we may lose orthogonality, have redundant signals in our dictionary and no longer have unique signal expansions; which is OK if we only care about sparsity.

The EMD expansion is sparse in the sense that you can express a signal as a linear combination of just a few IMFs.