Difference between PCA and ICA

PCA:

1) A set of orthogonal e vectors that are ranked by the projected variance

2) Top k e's give the best k-th order approximation of the v's in terms of variance explained

3) The e's are actually the eigenvectors of the covariance matrix of the v's.

ICA:

1) Similar idea but the e's do not have to be orthogonal

2) Don't have any nice "best k-th-order approximation" property