README.md

In alumbreras/NBMF: Bayesian Mean-parameterized Nonnegative Binary Matrix Factorization

Bayesian Mean-parameterized Nonnegative Binary Matrix Factorization

Code for our paper "Bayesian Mean-parameterized Nonnegative Binary Matrix Factorization"

Project structure

• R: algorithms in R. The main file is BernoulliNMF.R, that contains the function BernoulliNMF, that performs the inference of the latent matrices W and H. The inference method is chosen through the parameters. Both MCMC and VB methods are mostly writenn in Rcpp ('src' folder).

• src: implementation of MCMC and VB algorithms in Rcpp. These methods are called from the ones in the R folder.

• man: automatically generated folder with documentation of the R functions.

• data: datasets ready to be used.

• experiments: scripts to run the experiments.

Experiments

Experiments and figures are reproducible with the scripts in the experiments folder.

First, in order to get acquainted with the general workflow and functions, we suggest you go trough the file xp_paper_basic_example.R

The other files are:

• xp_paper_fig2_synthetic: generates synthetic data from the different generative models (Figure 2).

• xp_paper_fig5_originals: plots the datasets used in the experiments (Figure 3).

• xp_paper_fig6_reconstruct: infers the latent factors and plot the expectation of V (Figure 6).

• xp_paper_fig7-8-9_dictionaries: infers and plots the expectation of the dictionaries W (Figures 7 to 9).

• xp_paper_fig10_predictive_likelihood: infers the latent factors and posterior of V, and plots the log-likelihood on unobserved parts of the matrix (Figure 10).

• xp_paper_fig11_VB_convergences: runs VB algorithms and plots their predictive log-likelihood to compare speed of convergence (Figure 11).

alumbreras/NBMF documentation built on July 28, 2020, 9:58 a.m.