Summary of Joint Linked Component Analysis For Multiview Data, by Lin Xiao et al.
Joint Linked Component Analysis for Multiview Data
by Lin Xiao, Luo Xiao
First submitted to arxiv on: 17 Jun 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 joint linked component analysis (joint_LCA) is a new approach for multiview data that simultaneously identifies view-specific loading matrices and the rank of the common latent subspace. This method differs from classic sequential methods, which extract shared components one at a time. Joint_LCA uses matrix decomposition to model a joint structure and individual structures in each data view, allowing it to estimate cross-covariance between views. An objective function with a novel penalty term is proposed to achieve simultaneous estimation and rank selection. Additionally, a refitting procedure is used to reduce the shrinkage bias caused by penalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Joint LCA is a new way to work with different types of data together. It helps us find patterns that are unique to each type of data, but also finds common patterns that are shared between them. This is useful because it can help us better understand complex systems or make predictions about how different things will behave. |
Keywords
* Artificial intelligence * Objective function
