Summary of An Em Gradient Algorithm For Mixture Models with Components Derived From the Manly Transformation, by Katharine M. Clark and Paul D. Mcnicholas
An EM Gradient Algorithm for Mixture Models with Components Derived from the Manly Transformation
by Katharine M. Clark, Paul D. McNicholas
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel mixture modeling approach is introduced by Zhu and Melnykov (2018), which enables fitting complex models when component distributions are derived from the Manly transformation. The developed EM algorithm leverages Nelder-Mead optimization to update the skew parameter, λg. As an alternative, a one-step Newton’s method-based EM gradient algorithm is proposed, contingent on good initial estimates for model parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of fitting mixture models has been created by Zhu and Melnykov. This allows them to work with complex models when the individual parts are shaped by something called the Manly transformation. The team developed an Expectation-Maximization (EM) algorithm that uses Nelder-Mead optimization to update a special parameter. They also came up with an alternative method, using one step of Newton’s method, as long as the initial estimates for the model are good. |
Keywords
* Artificial intelligence * Optimization
