Note on the Equivalence of Orthogonalizing EM and Proximal Gradient Descent
Xiong et al. (2016) develop a method called orthogonalizing EM (OEM) to solve penalized regression problems for tall data. While OEM is developed in the context of the EM algorithm, we show that it is, in fact, an instance of proximal gradient descent, a popular first-order convex optimization algorithm.
Authors
- James Yang (ORCID: https://orcid.org/0000-0002-0015-7812)
- Trevor Hastie (ORCID: https://orcid.org/0000-0002-0164-3142)
Institutions
- Stanford University (US)
Publication Details
- Journal
- Technometrics
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1080/00401706.2024.2430204
- Primary Topic
- Sparse and Compressive Sensing Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- National Institutes of Health