### Abstract

Original language | English |
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Place of Publication | Birmingham, UK |

Publisher | Aston University |

Number of pages | 27 |

Publication status | Published - 2002 |

### Fingerprint

### Keywords

- hierarchical Generative Topographic Mapping
- interactive mode
- automatic mode
- magnification factors
- latent trait models
- unsupervised construction
- overlapping data

### Cite this

*Semi-supervised learning of hierarchical latent trait models for data visualisation*. Birmingham, UK: Aston University.

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**Semi-supervised learning of hierarchical latent trait models for data visualisation.** / Sun, Yi; Tino, Peter; Kaban, Ata; Nabney, Ian T.

Research output: Working paper › Technical report

TY - UNPB

T1 - Semi-supervised learning of hierarchical latent trait models for data visualisation

AU - Sun, Yi

AU - Tino, Peter

AU - Kaban, Ata

AU - Nabney, Ian T.

PY - 2002

Y1 - 2002

N2 - An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteHGTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKabanpami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteHGTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

AB - An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteHGTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKabanpami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteHGTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

KW - hierarchical Generative Topographic Mapping

KW - interactive mode

KW - automatic mode

KW - magnification factors

KW - latent trait models

KW - unsupervised construction

KW - overlapping data

M3 - Technical report

BT - Semi-supervised learning of hierarchical latent trait models for data visualisation

PB - Aston University

CY - Birmingham, UK

ER -