Abstract
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. The conventional supervised GTM uses classi.cation data whose teaching signals have no numerical meaning, and therefore cannot directly handle regression data whose teaching signals have numerical meaning. To overcome the problem, we propose a supervised GTM model which can naturally handle regression data. In order to handle miss-ing labels, we also propose a semi-supervised GTM model that uses both labeled and unlabeled data.
