High-order contrasts for independent component analysis
Neural Computation
An Information-Theoretic Approach to Neural Computing
An Information-Theoretic Approach to Neural Computing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Separation capability of overcomplete ICA approaches
SIP'07 Proceedings of the 6th Conference on 6th WSEAS International Conference on Signal Processing - Volume 6
Sequential fixed-point ica based on mutual information minimization
Neural Computation
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An improved method for independent component analysis based on the diagonalization of cumulant tensors is proposed. It is based on Comon's algorithm [1] but it takes third- and fourth-order cumulant tensors into account simultaneously. The underlying contrast function is also mathematically much simpler and has a more intuitive interpretation. It is therefore easier to optimize and approximate. A comparison with Comon's algorithm, JADE [2] and FastICA [3] on different data sets demonstrates its performance.