The Independence Assumption: Dependent Component Analysis
Redundancy reduction as a form of neural coding has been a topic of great research interest since the early sixties. A number of strategies have been proposed, but the one which is attracting most attention recently assumes that this coding is carried out so that the output signals are as independent as possible. In this work, we go one step further and propose an algorithm to separate non-orthogonal signals (i.e., dependent signals) based on the minimization of the output mutual spectral overlap. Indeed, separating independent sources turns to be a special case of this strategy. Moreover, we show that this principle can also be used to separate spectrally overlapping signals by exploiting their higher-order cyclostationary properties. We also suggest a numerically-efficient algorithm which searches for the learning step size in a way that avoids divergence.
KeywordsCoherence Assure Cyclone Autocorrelation Sine
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