Semi-Parametric Exponential Family PCA
Abstract
We present a semi-parametric latent variable model based technique for density modelling, dimensionality reduction and visualization. Unlike previous methods, we estimate the latent distribution non-parametrically which enables us to model data generated by an underlying low dimen- sional, multimodal distribution. In addition, we allow the components of latent variable models to be drawn from the exponential family which makes the method suitable for special data types, for example binary or count data. Simulations on real valued, binary and count data show fa- vorable comparison to other related schemes both in terms of separating different populations and generalization to unseen samples.
Cite
Text
Sajama and Orlitsky. "Semi-Parametric Exponential Family PCA." Neural Information Processing Systems, 2004.Markdown
[Sajama and Orlitsky. "Semi-Parametric Exponential Family PCA." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/sajama2004neurips-semiparametric/)BibTeX
@inproceedings{sajama2004neurips-semiparametric,
title = {{Semi-Parametric Exponential Family PCA}},
author = {Sajama, Sajama and Orlitsky, Alon},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1177-1184},
url = {https://mlanthology.org/neurips/2004/sajama2004neurips-semiparametric/}
}