Semisupervised learning of hierarchical latent trait models for data visualization

@article{Nabney2005SemisupervisedLO,
  title={Semisupervised learning of hierarchical latent trait models for data visualization},
  author={Ian T. Nabney and Yi Sun and Peter Ti{\~n}o and Ata Kab{\'a}n},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2005},
  volume={17},
  pages={384-400}
}
Recently, we have developed the hierarchical generative topographic mapping (HGTM), an interactive method for visualization of large high-dimensional real-valued data sets. We propose a more general visualization system by extending HGTM in three ways, which allows the user to visualize a wider range of data sets and better support the model development process. 1) We integrate HGTM with noise models from the exponential family of distributions. The basic building block is the latent trait… CONTINUE READING