Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
High-order contrasts for independent component analysis
Neural Computation
Neural Computation
Independent Component Analysis: Principles and Practice
Independent Component Analysis: Principles and Practice
Source separation in astrophysical maps using independent factor analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Topographic Independent Component Analysis
Neural Computation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Analysis of astrophysical ice analogs using regularized alternating least squares
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
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The analysis of astrophysical ices and the determination of the compounds that are present in the molecular clouds play a fundamental role in order to predict the future evolution of the cloud, e.g., its transformation to protostellar bodies or the appearance of new radicals and molecules. Because of the difficulties of obtaining satellite data, the process is simulated first in the laboratory generating ice analogs under well controlled variables. In this case, the ice mixture is carried on allocating the different components in the appropriate concentrations and recording the spectrum of the aggregated ice. This process tries to simulate the real process of forming ice mantles under the environmental conditions of the interstellar medium. The spectrum of each ice can be modeled as the linear instantaneous superposition of the spectrum of the different compounds, so a source separation approach is proper. In addition, some priors about the sources and the mixing matrix entries can be assumed, obtaining an informed Bayesian approach to the problem. We present the results obtained with the variational Bayesian approach for simulated and real mixtures, showing the good performance of the algorithm.