  Unsupervised integrative analysis of multiple data sources has become common
place and scalable algorithms are necessary to accommodate ever increasing
availability of data. Only few currently methods have estimation speed as their
focus, and those that do are only applicable to restricted data layouts such as
different data types measured on the same observation units. We introduce a
novel point of view on low-rank matrix integration phrased as a graph
estimation problem which allows development of a method, large-scale Collective
Matrix Factorization (lsCMF), which is able to integrate data in flexible
layouts in a speedy fashion. It utilizes a matrix denoising framework for rank
estimation and geometric properties of singular vectors to efficiently
integrate data. The quick estimation speed of lsCMF while retaining good
estimation of data structure is then demonstrated in simulation studies.
