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- W4385689059 abstract "This paper explores Higher-order Methods (HoM) for Matrix Factorization (MF) and Tensor Factorization (TF) models, which are powerful tools for high dimensional data analysis and feature extraction. Unlike First-order Methods (FoM), which use gradients, HoM use higher-order derivatives of the objective function, which makes them faster but more costly per iteration. We develop efficient and implementable higher-order proximal point methods within the BLUM framework for large-scale problems. We introduce the appropriate objective functions, the algorithm, and the experimental results that demonstrate the advantages of our HoM-based algorithms over FoM-based algorithms for MF and TF models. We show that our HoM-based algorithms have a lower number of iterations with respect to their per-iteration cost than FoM-based algorithms." @default.
- W4385689059 created "2023-08-10" @default.
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- W4385689059 date "2023-07-02" @default.
- W4385689059 modified "2023-09-27" @default.
- W4385689059 title "Inexact higher-order proximal algorithms for tensor factorization" @default.
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- W4385689059 doi "https://doi.org/10.1109/ssp53291.2023.10208064" @default.
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