Iterative and multi-level methods for Bayesian multirelational factorization with features KU Leuven
Machine Learning methods are increasingly important in society and industry. The amount of data available for these Machine Learning applications is growing exponentially. This excessive amount of data still has to be processed efficiently. Designing robust and scalable algorithms for these large-scale data sets becomes increasingly important.
Matrix factorization of an incompletely filled matrix is one of these applications which has ...