conference paper

Conference/Proceedings31st IEEE Picture Coding Symposium, Cairns, Australia
Start date31.05.2015
End date03.06.2015
Author(s)Thomas Sikora
TitleA Novel Kernel PCA/KLT Approach for Transform Coding of Waveforms
AbstractA novel Kernel PCA/Kernel KLT transform (S-KPCA) is introduced which incorporates higher order statistics into the design of the transform matrix using a Reproducing Kernel Hilbert Space (RKHS) approach. The goal is to arrive at an orthonormal transform matrix E with column eigenvectors that allow reconstruction of an input vector with few coefficients and superior signal fidelity. In contrast to the well known Kernel PCA the number of the generated transform coefficients is not dependent on the size of the training set and the “pre-image problem” is avoided completely. Results indicate that the derived transform is more compact than the standard PCA/KLT in terms of fidelity measures in RKHS.
Key wordsHilbert spaces, Eigenvalues and eigenfunctions, Image coding, Principal component analysis wavelet transforms