A novel Kernel PCA/KLT approach for transform coding of waveforms
A 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.