Alert button
Picture for Adam Prügel Bennett

Adam Prügel Bennett

Alert button

A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation

Add code
Bookmark button
Alert button
Jun 13, 2019
Steven Squires, Adam Prügel Bennett, Mahesan Niranjan

Figure 1 for A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation
Figure 2 for A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation
Figure 3 for A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation
Figure 4 for A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation

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.

Viaarxiv icon
Github IconAccess Paper or Ask Questions
Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon