Summary of Scalable Random Feature Latent Variable Models, by Ying Li et al.
Scalable Random Feature Latent Variable Models
by Ying Li, Zhidi Lin, Yuhao Liu, Michael Minyi Zhang, Pablo M. Olmos, Petar M. Djurić
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Random Feature Latent Variable Models (RFLVMs) are a state-of-the-art approach to handling non-Gaussian likelihoods and uncovering patterns in high-dimensional data. However, their reliance on Monte Carlo sampling leads to scalability issues for large datasets. To address this, the authors develop a novel optimization-based variational Bayesian inference (VBI) algorithm, block coordinate descent variational inference (BCD-VI), which enables scalable RFLVMs (SRFLVM). The proposed method demonstrates improved performance in generating informative latent representations and imputing missing data across various real-world datasets, outperforming state-of-the-art competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Random feature latent variable models are a way to understand patterns in big data. But they can be slow for really big datasets. To fix this, scientists created a new way to use variational Bayesian inference (VBI) with these models. This new method is called block coordinate descent variational inference (BCD-VI). It helps make the models faster and better at understanding patterns in data. The new method works well on real-world datasets and does better than other methods. |
Keywords
» Artificial intelligence » Bayesian inference » Inference » Optimization
