Abstract
The L1 norm has been tremendously popular in signal and imageprocessing in the past two decades due to its sparsity-promoting properties.More recently, its generalization to non-Euclidean domains has been founduseful in shape analysis applications.For example, in conjunction with the minimization of the Dirichlet energy,it was shown to produce a compactly supported quasi-harmonic orthonormal basis,dubbed as compressed manifold modes.The continuous L1 norm on the manifold is often replacedby the vector l1 norm applied to sampled functions.We show that such an approach is incorrect in the sense that it doesnot consistently discretize the continuous norm and warn against its sensitivity to the specific sampling.We propose two alternative discretizations resulting in aniteratively-reweighed l2 norm.We demonstrate the proposed strategy on the compressed modes problem,which reduces to a sequence of simple eigendecomposition problems notrequiring non-convex optimization on Stiefel manifolds and producing more stable and accurate results.