Fast CP model fitting with integrated ASD-ALS procedure

The CP decomposition is the most appropriate tool for mod- eling data arrays with a trilinear structure. Model fitting can be hindered by several issues, including computational inefficiency, bad initialization, excessive modeled noise, sensitivity to over-factoring and collinearity. Many algorithms have been proposed for parameter estimation, each with specific strengths and weaknesses. Fast procedures tend to be less stable and vice-versa. Stability is usually prioritized by preferring the least-square approach ALS, albeit slow and sensitive to excess factors. As a solution integrated methods have been proposed in the literature. First, estimation is initialized with a fast procedure to ensure competi- tive speed then results are refined with ALS to improve precision. In this work, we implement a novel integrated algorithm called INT-3 where ASD steps are concatenated with ALS. ASD was selected because of its remarkable speed and low memory consumption requirements. INT-3 performance is tested against ALS on artificial data.