Probabilistic Visualisation of High-Dimensional Binary Data
Abstract
We present a probabilistic latent-variable framework for data visu(cid:173) alisation, a key feature of which is its applicability to binary and categorical data types for which few established methods exist. A variational approximation to the likelihood is exploited to derive a fast algorithm for determining the model parameters. Illustrations of application to real and synthetic binary data sets are given.
Cite
Text
Tipping. "Probabilistic Visualisation of High-Dimensional Binary Data." Neural Information Processing Systems, 1998.Markdown
[Tipping. "Probabilistic Visualisation of High-Dimensional Binary Data." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/tipping1998neurips-probabilistic/)BibTeX
@inproceedings{tipping1998neurips-probabilistic,
title = {{Probabilistic Visualisation of High-Dimensional Binary Data}},
author = {Tipping, Michael E.},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {592-598},
url = {https://mlanthology.org/neurips/1998/tipping1998neurips-probabilistic/}
}