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This article presents FastManly, a novel algorithmic optimization technique for mixtures of Manly transformations that replaces the standard Nelder-Mead method with Newton's method within an EM gradient framework. By deriving explicit gradients and full Hessians, the approach achieves significant computational speedups, offering a more efficient tool for statistical modeling tasks relevant to machine learning practitioners.
FastManly: An EM-Gradient Algorithm for Manly Mixture Models
arXiv:2410.00848v2 Announce Type: replace-cross Abstract: A faster implementation of mixtures of Manly transformations is proposed. This method, called FastManly, uses Newton's method for optimization in an EM gradient algorithm instead of Nelder-Mead in a traditional EM. A gradient and full Hessian are derived. Simulations show improved performance with noticeable speedups.
Abstract:A faster implementation of mixtures of Manly transformations is proposed. This method, called FastManly, uses Newton's method for optimization in an EM gradient algorithm instead of Nelder-Mead in a traditional EM. A gradient and full Hessian are derived. Simulations show improved performance with noticeable speedups.
Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
arXiv:2410.00848 [stat.ML]
(or arXiv:2410.00848v2 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2410.00848 Focus to learn more arXiv-issued DOI via DataCite
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