Abstract
An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteH<sub>G</sub>TM 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 ¸iteKaban<sub>p</sub>ami. 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 ¸iteH<sub>G</sub>TM, 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.
Original language | English |
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Place of Publication | Birmingham, UK |
Publisher | Aston University |
Number of pages | 27 |
Publication status | Published - 2002 |
Keywords
- hierarchical Generative Topographic Mapping
- interactive mode
- automatic mode
- magnification factors
- latent trait models
- unsupervised construction
- overlapping data