Horseshoe-type Priors for Independent Component Estimation
<|reference_start|>Horseshoe-type Priors for Independent Component Estimation: Independent Component Estimation (ICE) has many applications in modern day machine learning as a feature engineering extraction method. Horseshoe-type priors are used to provide scalable algorithms that enables both point estimates via expectation-maximization (EM) and full posterior sampling via Markov Chain Monte Carlo (MCMC) algorithms. Our methodology also applies to flow-based methods for nonlinear feature extraction and deep learning. We also discuss how to implement conditional posteriors and envelope-based methods for optimization. Through this hierarchy representation, we unify a number of hitherto disparate estimation procedures. We illustrate our methodology and algorithms on a numerical example. Finally, we conclude with directions for future research.<|reference_end|>
@article{datta2024horseshoe-type,
    title={Horseshoe-type Priors for Independent Component Estimation},
    author={Jyotishka Datta and Nicholas G. Polson},
    journal={arXiv preprint arXiv:2406.17058},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17058},
    primaryClass={stat.ME cs.LG}
}