Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis by general nonlinear Hebbian-like learning rules
Signal Processing - Special issue on neural networks
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Independent component analysis: algorithms and applications
Neural Networks
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Activity index variance as an indicator of the number of signal sources
WSEAS Transactions on Signal Processing
On-line content-based image retrieval system using joint querying and relevance feedback scheme
WSEAS Transactions on Computers
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Independent component analysis is a generative model for observed multivariate data, which are assumed to be mixtures of some unknown latent variables. It is a statistical and computational technique for revealing hidden factors that underlies set of random variable measurements of signals. A common problem faced in the disciplines such as statistics, data analysis, signal processing and neural network is finding a suitable representation of multivariate data. The objective of ICA is to represent a set of multidimensional measurement vectors in a basis where the components are statistically independent. In the present paper we deal with a set of images that are mixed randomly. We apply the principle of uncorrelatedness and minimum entropy to find ICA. The original images are then retrieved using fixed point algorithm known as FastICA algorithm and compared with the original images with the help of estimated error. The outputs from the intermediate steps of algorithm such as PCA, Whitening matrix, Convergence of algorithm and dewhitening matrix are also discussed.