Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix.
|Author||Arto Klami and Lauri Väre|
|Date of publication||2014-03-25 14:26:42|
|Maintainer||Arto Klami <firstname.lastname@example.org>|
|License||GPL (>= 2)|