Summary of Hyperboloid Gplvm For Discovering Continuous Hierarchies Via Nonparametric Estimation, by Koshi Watanabe et al.
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation
by Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 hyperboloid Gaussian process (GP) latent variable models (hGP-LVMs) aim to embed high-dimensional hierarchical data with implicit continuity via nonparametric estimation. By adopting generative modeling using the GP, this approach enables effective hierarchical embedding and alleviates ill-posed hyperparameter tuning. The paper presents three variants employing original point, sparse point, and Bayesian estimations, along with learning algorithms incorporating Riemannian optimization and active approximation scheme of GP-LVM. Additionally, a reparameterization trick is introduced for Bayesian latent variable learning. Evaluation on several datasets demonstrates the ability of hGP-LVMs to represent high-dimensional hierarchies in low-dimensional spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand complex data by creating a simpler version of it using something called dimensionality reduction. Dimensionality reduction is like taking a big picture and shrinking it down to fit on a smaller piece of paper, but still keeping the important parts. The new way they do this is called hyperboloid Gaussian process (GP) latent variable models or hGP-LVMs for short. It’s like a special tool that helps us find patterns in data and make sense of it. |
Keywords
» Artificial intelligence » Dimensionality reduction » Embedding » Hyperparameter » Optimization
