Noisy and Incomplete Boolean Matrix Factorizationvia Expectation Maximization
Machine Learning
2019-05-31 v1 Machine Learning
Abstract
Probabilistic approach to Boolean matrix factorization can provide solutions robustagainst noise and missing values with linear computational complexity. However,the assumption about latent factors can be problematic in real world applications.This study proposed a new probabilistic algorithm free of assumptions of latentfactors, while retaining the advantages of previous algorithms. Real data experimentshowed that our algorithm was favourably compared with current state-of-the-artprobabilistic algorithms.
Cite
@article{arxiv.1905.12766,
title = {Noisy and Incomplete Boolean Matrix Factorizationvia Expectation Maximization},
author = {Lifan Liang and Songjian Lu},
journal= {arXiv preprint arXiv:1905.12766},
year = {2019}
}