Paper Info

Title | ||
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Nonlinear Independent Component Analysis by Self-Organizing Maps |

Abstract | ||
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Linear Independent Component Analysis considers the problem of finding a linear transformation that makes the components of the output vector statistically independent. This can be applied to blind source separation, where the input data consist of unknown linear mixtures of unknown independent source signals. The original source signals can be recovered from their mixtures using the assumption that they are statistically independent. More generally we can consider nonlinear mappings that make... |

Year | DOI | Venue |
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1996 | 10.1007/3-540-61510-5_137 | ICANN |

Keywords | Field | DocType |

nonlinear independent component analysis,self-organizing maps,linear transformation,statistical independence,blind source separation,linear independence,independent component analysis,data consistency | Linear independence,Nonlinear system,Pattern recognition,Self-organizing map,Independent component analysis,Artificial intelligence,Linear map,Component analysis,Blind signal separation,Independence (probability theory),Mathematics | Conference |

ISBN | Citations | PageRank |

3-540-61510-5 | 6 | 0.61 |

References | Authors | |

10 | 1 |