Advances in multi-view matrix factorizations
Yifeng Li · 2016
As the emergement of high-throughput measurement technologies, we are entering the big data era. Modern data are often generated from heterogeneous multiple sources, thus can be called multi-view data. The challenge of effectively integrating such data for decision making and novel knowledge discovery is raised. Matrix factorization methods have historically played important roles in various analyses of single-view data sets. These models have been recently generalized for the analysis of multi-view data in a few areas. In order to better understand the theories and applications of these models, as well as inspire new studies in this field, in this paper, we discuss multi-view matrix factorization models mainly from a Bayesian view point using the same notation system.