## Abstract

Independent component analysis (ICA) is an important topic of signal processing and neural network which transforms an observed multidimensional random vector into components that are mutually as independent as possible. In this paper, we have introduced a new method called SwiPe-ICA (Stepwise Pearsonian ICA) that combines the methodology of projection pursuit with Pearsonian density estimation. Pearsonian density function instead of the classical polynomial density expansions is employed to approximate the density along each one-dimensional projection using differential entropy. This approximation of entropy is more exact than the classical approximation based on the polynomial density expansions when the source signals are supergaussian. The validity of the new algorithm is verified by computer simulation.

Original language | English (US) |
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Pages (from-to) | 74-81 |

Number of pages | 8 |

Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Volume | 5441 |

DOIs | |

State | Published - Jun 19 2009 |

Event | 8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009 - Paraty, Brazil Duration: Mar 15 2009 → Mar 18 2009 |

## Keywords

- Blind signal separation
- Independent component analysis
- Pearsonian density function