Abstract
The CP decomposition is the most appropriate tool for modeling 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 competitive 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.
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Todorov, V., Simonacci, V., Gallo, M., Trendafilov, N. (2023). Fast CP Model Fitting with Integrated ASD-ALS Procedure. In: García-Escudero, L.A., et al. Building Bridges between Soft and Statistical Methodologies for Data Science . SMPS 2022. Advances in Intelligent Systems and Computing, vol 1433. Springer, Cham. https://doi.org/10.1007/978-3-031-15509-3_49
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DOI: https://doi.org/10.1007/978-3-031-15509-3_49
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