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Found 6 papers, 2 papers with code
Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
Motivated by the lack of asymmetry in the Riemannian metric of the embedding space, this paper extends the MDS problem to a natural asymmetric generalisation of Riemannian manifolds called Finsler manifolds.
Coupled Hierarchical Structure Learning using Tree-Wasserstein Distance
However, existing methods for learning these latent structures typically focus on either samples or features, ignoring possible coupling between them.
Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy
First, our TWD is specifically designed for data with a latent feature hierarchy, i. e., the features lie in a hierarchical space, in contrast to the usual focus on embedding samples in hyperbolic space.
Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
The proposed NLSFs are based on a new form of spectral domain that is transferable between graphs.
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
Finding meaningful representations and distances of hierarchical data is important in many fields.
Hyperbolic Procrustes Analysis Using Riemannian Geometry
Here, we take a purely geometric approach for label-free alignment of hierarchical datasets and introduce hyperbolic Procrustes analysis (HPA).
