Semi-supervised learning of hierarchical latent trait models for data visualisation
Sun, Yi; Tino, Peter; Kaban, Ata; Nabney, Ian T.
Aston University
English
Array
JObject
An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteH_{G}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_{p}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_{G}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.
Metrics
Download from
Aston Publications Explorer (http://publications.aston.ac.uk/1326/1/NCRG_2002_012.pdf)