Summary of D-cdlf: Decomposition Of Common and Distinctive Latent Factors For Multi-view High-dimensional Data, by Hai Shu
D-CDLF: Decomposition of Common and Distinctive Latent Factors for Multi-view High-dimensional Data
by Hai Shu
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 A novel approach is proposed for decomposing multiple high-dimensional data views into their common-source, distinctive-source, and noise components. The method, called Decomposition of Common and Distinctive Latent Factors (D-CDLF), focuses on achieving uncorrelatedness between the common latent factors and distinctive latent factors within a single view, as well as between distinctive latent factors across different views. This paper discusses the estimation of D-CDLF in high-dimensional settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have multiple datasets that you want to analyze together. One way to do this is to break each dataset into three parts: something they all share, something unique to each dataset, and some random noise. Most methods for doing this focus on making sure the shared part isn’t connected to the unique parts within a single dataset, but don’t worry about whether the unique parts from different datasets are also separate. This paper proposes a new method that fixes this problem by ensuring both types of separation. It then talks about how to use this method when working with really big datasets. |
