Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive blind separation of independent sources: a deflation approach
Signal Processing
Self-organizing maps
Independent component analysis by general nonlinear Hebbian-like learning rules
Signal Processing - Special issue on neural networks
Neural Computation
Modeling surround suppression in V1 neurons with a statistically-derived normalization model
Proceedings of the 1998 conference on Advances in neural information processing systems II
Independent component analysis: algorithms and applications
Neural Networks
Sparse Distributed Memory
The Problem of Sparse Image Coding
Journal of Mathematical Imaging and Vision
Topographic Independent Component Analysis
Neural Computation
Learning Overcomplete Representations
Neural Computation
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
A class of neural networks for independent component analysis
IEEE Transactions on Neural Networks
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
The Problem of Sparse Image Coding
Journal of Mathematical Imaging and Vision
Energy-based models for sparse overcomplete representations
The Journal of Machine Learning Research
Energy-based models for sparse overcomplete representations
The Journal of Machine Learning Research
An Adaptive Method for Subband Decomposition ICA
Neural Computation
Overcomplete topographic independent component analysis
Neurocomputing
An Overcomplete ICA Algorithm by InfoMax and InfoMin
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Compromising anonymous communication systems using blind source separation
ACM Transactions on Information and System Security (TISSEC)
A simple overcomplete ICA algorithm by non-orthogonal pair optimizations
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Bayesian estimation of overcomplete independent feature subspaces for natural images
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Properties of activity index extended by higher-order moments
CSS'10 Proceedings of the 4th international conference on Circuits, systems and signals
Partial extraction of edge filters by cumulant-based ICA under highly overcomplete model
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Separation theorem for independent subspace analysis and its consequences
Pattern Recognition
An easily computable eight times overcomplete ICA method for image data
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
PET'05 Proceedings of the 5th international conference on Privacy Enhancing Technologies
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Estimating overcomplete ICA bases for image windows is a difficult problem. Most algorithms require the estimation of values of the independent components which leads to computationally heavy procedures. Here we first review the existing methods, and then introduce two new algorithms that estimate an approximate overcomplete basis quite fast in a high-dimensional space. The first algorithm is based on the prior assumption that the basis vectors are randomly distributed in the space, and therefore close to orthogonal. The second replaces the conventional orthogonalization procedure by a transformation of the marginal density to gaussian.