AI RESEARCH
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation
arXiv CS.LG
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ArXi:2410.16698v2 Announce Type: replace Dimensionality reduction (DR) offers a useful representation of complex high-dimensional data. Recent DR methods focus on hyperbolic geometry to derive a faithful low-dimensional representation of hierarchical data. However, existing methods are based on neighbor embedding, frequently ruining the continual relation of the hierarchies. This paper presents hyperboloid Gaussian process (GP) latent variable models (hGP-LVMs) to embed high-dimensional hierarchical data with implicit continuity via nonparametric estimation.