Summary of Empirical Bayes Linked Matrix Decomposition, by Eric F. Lock
Empirical Bayes Linked Matrix Decomposition
by Eric F. Lock
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed empirical variational Bayesian approach for integrative matrix factorization offers a flexible solution for identifying shared signal across multiple matrices or specific to a given matrix. This method accommodates bidimensional integration, yields shrinkage for inferred signals, and has an efficient estimation algorithm with no tuning parameters. The paper establishes conditions for the uniqueness of the underlying decomposition for a broad family of methods that includes the proposed approach. Additionally, it describes an iterative imputation approach for missing data in single-matrix and linked matrix contexts. Extensive simulations show the method’s effectiveness in recovering low-rank signal, decomposing shared and specific signals, and imputing missing data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to combine information from different sources of data that are connected in special ways. This is useful for studying things like genes and proteins in cells, where we might have information from different types of experiments or samples. The researchers propose a new way to do this combining that works well even when some of the data is missing. They test their method on real-world data and show it can give us valuable insights about how the different parts of cells are working together. |
