Theory of Matching Pursuit in Kernel Defined Feature Spaces
Published on Dec 20, 20084859 Views
We analyse matching pursuit for kernel principal components analysis (KPCA) by proving that the sparse subspace it produces is a sample compression scheme. We show that this bound is tighter than the