7 research outputs found
Regularized and Smooth Double Core Tensor Factorization for Heterogeneous Data
We introduce a general tensor model suitable for data analytic tasks for
heterogeneous data sets, wherein there are joint low-rank structures within
groups of observations, but also discriminative structures across different
groups. To capture such complex structures, a double core tensor (DCOT)
factorization model is introduced together with a family of smoothing loss
functions. By leveraging the proposed smoothing function, the model accurately
estimates the model factors, even in the presence of missing entries. A
linearized ADMM method is employed to solve regularized versions of DCOT
factorizations, that avoid large tensor operations and large memory storage
requirements. Further, we establish theoretically its global convergence,
together with consistency of the estimates of the model parameters. The
effectiveness of the DCOT model is illustrated on several real-world examples
including image completion, recommender systems, subspace clustering and
detecting modules in heterogeneous Omics multi-modal data, since it provides
more insightful decompositions than conventional tensor methods