Abstract
Blind signal separation (BSS) is a powerful technique for separation of mixed signals with weak assumptions on the incoming signals. The objectives of BSS are analogous to the objectives of exploratory projection pursuit which is widely used in the statistical community for finding structure in high dimensional data sets. In this paper, we adapt exploratory projection pursuit for BSS. First, we introduce exploratory projection pursuit and the associated projection pursuit index (PPI). We adapt the PPI for application to BSS. We also investigate the order of approximation required to achieve satisfactory separation using the PPI, and compare its performance to a maximum-likelihood BSS technique using a Gram-Charlier expansion.
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