Nonnegative PARAFAC2: a flexible coupling approach
This work addresses a specific limitation in tensor decomposition for source separation, enabling nonnegativity constraints in PARAFAC2, which is incremental for applications like chemometrics.
The authors tackled the challenge of modeling variability in tensor decomposition by introducing a relaxation of the PARAFAC2 model that allows nonnegativity constraints on the varying mode, and they derived an algorithm to compute it, studying performance on synthetic and chemometrics data.
Modeling variability in tensor decomposition methods is one of the challenges of source separation. One possible solution to account for variations from one data set to another, jointly analysed, is to resort to the PARAFAC2 model. However, so far imposing constraints on the mode with variability has not been possible. In the following manuscript, a relaxation of the PARAFAC2 model is introduced, that allows for imposing nonnegativity constraints on the varying mode. An algorithm to compute the proposed flexible PARAFAC2 model is derived, and its performance is studied on both synthetic and chemometrics data.