A novel estimation procedure for robust CANDECOMP/PARAFAC model fitting

The usual way of parameter estimation in CP is an alternating least squares
(ALS) procedure that yields least-squares solutions and provides consistent
outcomes but at the same time has several deficiencies, like sensitivity to
the presence of outliers in the data, slow convergence, and susceptibility to
degeneracy conditions. A number of works have addressed these weaknesses,
but to our knowledge, there is no outlier-robust procedure that is highly
computationally efficient at the same time, especially for large data sets. We
propose a robust procedure based on an integrated estimation algorithm,
alternative to ALS, which guards against outliers and is computationally
efficient at the same time.