Liver CT Annotation via Generalized Coupled Tensor Factorization

This study deals with the missing answers prediction problem. We address this problem using coupled analysis of ImageCLEF2014 dataset by representing it as a heterogeneous data, i.e., dataset in the form of matrices. We propose to use an approach based on probabilistic interpretation of tensor factorization models, i.e., Generalized Coupled Tensor Factorization, which can simultaneously fit a large class of matrix/tensor models to higher-order matrices/tensors with common latent factors using different loss functions. Numerical experiments demonstrate that joint analysis of data from multiple sources via coupled factorization gives high prediction performance.