### Abstract

Original language | English |
---|---|

Title of host publication | Artificial Neural Networks — ICANN 2001 |

Editors | G. Dorffner, H. Bischof, K. Hornik. |

Publisher | Springer |

Pages | 421-428 |

Number of pages | 8 |

Volume | 2130 |

ISBN (Print) | 3540424865, 9783540446682 |

DOIs | |

Publication status | Published - 1 Jan 2001 |

Event | International Conference on Artificial Neural Networks, ICANN 2001 - Vienna, Austria Duration: 21 Aug 2001 → 25 Aug 2001 |

### Publication series

Name | Lecture Notes in Computer Science |
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Publisher | Springer-Verlag |

### Conference

Conference | International Conference on Artificial Neural Networks, ICANN 2001 |
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Country | Austria |

City | Vienna |

Period | 21/08/01 → 25/08/01 |

### Fingerprint

### Bibliographical note

The original publication is available at www.springerlink.com### Keywords

- data visualization
- non-linear projection manifolds
- directional curvatures
- curvature plots
- geometry
- visualization plots

### Cite this

*Artificial Neural Networks — ICANN 2001*(Vol. 2130, pp. 421-428). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/3-540-44668-0_59

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*Artificial Neural Networks — ICANN 2001.*vol. 2130, Lecture Notes in Computer Science, Springer, pp. 421-428, International Conference on Artificial Neural Networks, ICANN 2001, Vienna, Austria, 21/08/01. https://doi.org/10.1007/3-540-44668-0_59

**Using directional curvatures to visualize folding patterns of the GTM projection manifolds.** / Tino, Peter; Nabney, Ian T.; Sun, Yi.

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

TY - GEN

T1 - Using directional curvatures to visualize folding patterns of the GTM projection manifolds

AU - Tino, Peter

AU - Nabney, Ian T.

AU - Sun, Yi

N1 - The original publication is available at www.springerlink.com

PY - 2001/1/1

Y1 - 2001/1/1

N2 - In data visualization, characterizing local geometric properties of non-linear projection manifolds provides the user with valuable additional information that can influence further steps in the data analysis. We take advantage of the smooth character of GTM projection manifold and analytically calculate its local directional curvatures. Curvature plots are useful for detecting regions where geometry is distorted, for changing the amount of regularization in non-linear projection manifolds, and for choosing regions of interest when constructing detailed lower-level visualization plots.

AB - In data visualization, characterizing local geometric properties of non-linear projection manifolds provides the user with valuable additional information that can influence further steps in the data analysis. We take advantage of the smooth character of GTM projection manifold and analytically calculate its local directional curvatures. Curvature plots are useful for detecting regions where geometry is distorted, for changing the amount of regularization in non-linear projection manifolds, and for choosing regions of interest when constructing detailed lower-level visualization plots.

KW - data visualization

KW - non-linear projection manifolds

KW - directional curvatures

KW - curvature plots

KW - geometry

KW - visualization plots

UR - http://www.scopus.com/inward/record.url?scp=84958980948&partnerID=8YFLogxK

UR - http://www.springerlink.com/content/cyu9guhx8runkvrj/?p=06df1fa4ed45491e84cb0cc282826dbf&pi=0

U2 - 10.1007/3-540-44668-0_59

DO - 10.1007/3-540-44668-0_59

M3 - Conference contribution

SN - 3540424865

SN - 9783540446682

VL - 2130

T3 - Lecture Notes in Computer Science

SP - 421

EP - 428

BT - Artificial Neural Networks — ICANN 2001

A2 - Dorffner, G.

A2 - Bischof, H.

A2 - Hornik., K.

PB - Springer

ER -