A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation [article]

Steven Squires, Adam Prügel Bennett, Mahesan Niranjan
2019 arXiv   pre-print
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
more » ... 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.
arXiv:1906.05912v1 fatcat:4to4yx5itfcabmafz57mnanha4