### Abstract

In this paper a generalization of Post Nonlinear Independent Component Analysis (PNL-ICA) to Post Nonlinear Independent Subspace Analysis (PNL-ISA) is presented. In this framework sources to be identified can be multidimensional as well. For this generalization we prove a separability theorem: the ambiguities of this problem are essentially the same as for the linear Independent Subspace Analysis (ISA). By applying this result we derive an algorithm using the mirror structure of the mixing system. Numerical simulations are presented to illustrate the efficiency of the algorithm.

Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Pages | 677-686 |

Number of pages | 10 |

Volume | 4668 LNCS |

Edition | PART 1 |

Publication status | Published - 2007 |

Event | 17th International Conference on Artificial Neural Networks, ICANN 2007 - Porto, Portugal Duration: Sep 9 2007 → Sep 13 2007 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Number | PART 1 |

Volume | 4668 LNCS |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 17th International Conference on Artificial Neural Networks, ICANN 2007 |
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Country | Portugal |

City | Porto |

Period | 9/9/07 → 9/13/07 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science

### Cite this

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(PART 1 ed., Vol. 4668 LNCS, pp. 677-686). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4668 LNCS, No. PART 1).

**Post nonlinear independent subspace analysis.** / Szabó, Zoltán; Póczos, Barnabás; Szirtes, Gábor; Lőrincz, A.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*PART 1 edn, vol. 4668 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 4668 LNCS, pp. 677-686, 17th International Conference on Artificial Neural Networks, ICANN 2007, Porto, Portugal, 9/9/07.

}

TY - GEN

T1 - Post nonlinear independent subspace analysis

AU - Szabó, Zoltán

AU - Póczos, Barnabás

AU - Szirtes, Gábor

AU - Lőrincz, A.

PY - 2007

Y1 - 2007

N2 - In this paper a generalization of Post Nonlinear Independent Component Analysis (PNL-ICA) to Post Nonlinear Independent Subspace Analysis (PNL-ISA) is presented. In this framework sources to be identified can be multidimensional as well. For this generalization we prove a separability theorem: the ambiguities of this problem are essentially the same as for the linear Independent Subspace Analysis (ISA). By applying this result we derive an algorithm using the mirror structure of the mixing system. Numerical simulations are presented to illustrate the efficiency of the algorithm.

AB - In this paper a generalization of Post Nonlinear Independent Component Analysis (PNL-ICA) to Post Nonlinear Independent Subspace Analysis (PNL-ISA) is presented. In this framework sources to be identified can be multidimensional as well. For this generalization we prove a separability theorem: the ambiguities of this problem are essentially the same as for the linear Independent Subspace Analysis (ISA). By applying this result we derive an algorithm using the mirror structure of the mixing system. Numerical simulations are presented to illustrate the efficiency of the algorithm.

UR - http://www.scopus.com/inward/record.url?scp=38349015012&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=38349015012&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:38349015012

SN - 9783540746898

VL - 4668 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 677

EP - 686

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -