IMPUTATION OF COUPLED TENSORS AND GRAPHS

Abstract

Joint analysis of data from different sources can potentially improve one’s ability to reveal latent structure in heterogeneous datasets. For instance, social network activities and user demographic information can be leveraged to improve recommendations. However, the incompleteness and heterogeneity of the data challenge joint factorization of multiple datasets. Aspiring to address these challenges, the coupled graph tensor factorization model accounts for side information available in the form of correlation matrices or graphs. Here, a novel ADMM-based approach is put forth to impute missing entries and unveil hidden structure in the data. The iterative solver enjoys closed-form updates that result in reduced computational complexity. Numerical tests with synthetic and real data corroborate the merits of the proposed method relative to competing alternatives.