Noisy and Incomplete Boolean Matrix Factorizationvia Expectation Maximization

Probabilistic approach to Boolean matrix factorization can provide solutions robust against 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 latent factors, while retaining the advantages of previous algorithms. Real data experiment showed that our algorithm was favourably compared with current state-of-the-art probabilistic algorithms.