Comparison of Hebbian learning methods for image compression using the mixture of principal components network
R.D. Dony · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
A number of novel adaptive image compression methods have been developed using a new approach to data representation, a mixture of principal components (MPC). MPC, together with principal component analysis and vector quantization, form a spectrum of representations. The MPC network partitions the space into a number of regions or subspaces. Within each subspace the data are represented by the M principal components of the subspace. While Hebbian learning has been effectively used to extract principal components for the MPC, its stability is still a concern in practice. As a result, computationally more expensive methods such as batch eigendecomposition have produced more consistent results. This paper compares the performance of a number of Hebbian- based training schemes for the MPC network. These include training the entire network, network growing techniques, and a new tree-structured method. In the new tree-structured approach, each level in the tree, M, corresponds to an M- dimensional representation. A node and all its M - 1 parents represents a single M-dimensional subspace or class. The evaluation shows that the use of tree-structured approach improves training and results in reduced squared error.