Independent Component Analysis using Reinforcement Learning
نویسنده
چکیده
Recently Fyfe has used the REINFORCE algorithm to create a variety of topology-preserving mappings and to create valid projections for principal component analysis and canonical correlation analysis. In this paper, we extend his results to the case of independent component analysis. We illustrate the basic method with a number of different reward functions and a number of artificial and real data sets.
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