A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation
Machine Learning
2019-06-17 v1 Machine Learning
Abstract
We introduce and demonstrate the variational autoencoder (VAE) for probabilistic non-negative matrix factorisation (PAE-NMF). We design a network which can perform non-negative matrix factorisation (NMF) and add in aspects of a VAE to make the coefficients of the latent space probabilistic. By restricting the weights in the final layer of the network to be non-negative and using the non-negative Weibull distribution we produce a probabilistic form of NMF which allows us to generate new data and find a probability distribution that effectively links the latent and input variables. We demonstrate the effectiveness of PAE-NMF on three heterogeneous datasets: images, financial time series and genomic.
Cite
@article{arxiv.1906.05912,
title = {A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation},
author = {Steven Squires and Adam Prügel Bennett and Mahesan Niranjan},
journal= {arXiv preprint arXiv:1906.05912},
year = {2019}
}