Visualizing regression data by supervised Generative Topographic Mapping
Nobuhiko Yamaguchi · 2014
Generative Topographic Mapping (GTM) is a nonlinear latent variable model introduced by Bishop et al. as a data visualization technique. In this paper, we propose a supervised GTM model and a semi-supervised GTM model. Conventional supervised GTM models use discrete class labels in classification problems, and therefore cannot directly handle continuous output labels in regression problems. To overcome the problem, we propose a supervised GTM model which can naturally handle continuous output labels in regression problems. In order to handle missing labels, we also propose a semi-supervised GTM model that uses both labeled and unlabeled data.