Rootlets Hierarchical Principal Component Analysis for Revealing Nested Dependencies in Hierarchical Data

Rootlets Hierarchical Principal Component Analysis for Revealing Nested Dependencies in Hierarchical Data

Hierarchical clustering analysis (HCA) is a widely used unsupervised learning method. Limitations of HCA, however, include imposing an artificial hierarchy onto non-hierarchical data and fixed two-way mergers at every level. To address this, the current work describes a novel rootlets hierarchical p...

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Bibliographic Details
Main Authors: Korey P. Wylie, Jason R. Tregellas
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Mathematics
Subjects:
eigendecomposition
multivariate statistics
hyperbolic manifold
Riemannian geometry
manifold learning
Online Access:https://www.mdpi.com/2227-7390/13/1/72
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