Adaptive combination of PCA and VQ networks

In this paper we consider the principal component analysis (PCA) and vector quantization (VQ) neural networks for image compression. We present a method where the PCA and VQ steps are adaptively combined. A learning algorithm for this combined network is derived. We demonstrate that this approach can improve the results of the successive application of the individually optimal methods.     

SVD Algorithms: APEX-like versus Subspace Methods

We compare several new SVD learning algorithms which are based on the subspace method in principal component analysis with the APEX-like algorithm proposed by Diamantaras. It is shown experimentally that the convergence of these algorithms is as fast as the convergence of the APEX-like algorithm.     

Local PCA algorithms

Within the last years various principal component analysis (PCA) algorithms have been proposed. In this paper we use a general framework to describe those PCA algorithms which are based on Hebbian learning. For an important subset of these algorithms, the local algorithms, we fully describe their equilibria, where all lateral connections are set to zero and their local stability. We show how the parameters in the PCA algorithms have to be chosen in order to get an algorithm which converges to a stable equilibrium which provides principal component extraction.