Toward a theory of the striate cortex
Neural Computation
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
High-order contrasts for independent component analysis
Neural Computation
An efficient MDS-based topographic mapping algorithm
Neurocomputing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Extraction of Approximate Independent Components from Large Natural Scenes
Neural Information Processing
Joint Approximate Diagonalization Utilizing AIC-Based Decision in the Jacobi Method
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
A simple overcomplete ICA algorithm by non-orthogonal pair optimizations
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Linear mltilayer ICA using adaptive PCA
Neural Processing Letters
Image similarity based on hierarchies of ICA mixtures
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
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In this letter, a new ICA algorithm, linear multilayer ICA (LMICA), is proposed. There are two phases in each layer of LMICA. One is the mapping phase, where a two-dimensional mapping is formed by moving more highly correlated (nonindependent) signals closer with the stochastic multidimensional scaling network. Another is the local-ICA phase, where each neighbor (namely, highly correlated) pair of signals in the mapping is separated by MaxKurt algorithm. Because in LMICA only a small number of highly correlated pairs have to be separated, it can extract edge detectors efficiently from natural scenes. We conducted numerical experiments and verified that LMICA generates hierarchical edge detectors from large-size natural scenes.