arXiv Machine Learning
MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
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arXiv AI
Sep 10
Hypersolid: Emergent Vision Representations via Short-Range Repulsion
The paper introduces Hypersolid, a self‑supervised learning objective that uses short‑range repulsion to prevent representation collapse. It combines view alignment with local collision avoidance, creating a latent geometry of compact, semantically aligned neighborhoods with low anisotropy. This geometry improves unsupervised clustering and fine‑grained separation, though it reduces transferability.
arXiv Machine Learning
Jun 4
On Out-of-sample Embedding in UMAP
arXiv:2606. 04451v1 Announce Type: new Abstract: Neighbor embedding algorithms reveal correlations in high-dimensional data by constructing an equivalent graph representation in a lower-dimensional space.
arXiv AI
Aug 18
Shape Operator PCA: Curvature-Aware Projections for Geometric Machine Learning
arXiv:2608. 15313v1 Announce Type: cross Abstract: In this paper, we propose SHOPCA (Shape Operator-based Principal Component Analysis), a novel method for unsupervised metric learning and dimensionality reduction that incorporates differential geometric information into the covariance structure of classical PCA.
arXiv Machine Learning
Jul 13
Cluster and then Embed: A Modular Approach for Visualization
arXiv:2509. 03373v2 Announce Type: replace Abstract: Dimensionality reduction methods such as t-SNE and UMAP are popular methods for visualizing data with a potential (latent) clustered structure.
arXiv Machine Learning
Sep 1
Partially Linear Autoencoders for Manifold Learning and Dimensionality Reduction
arXiv:2608.29867v1 Announce Type: new Abstract: Autoencoders are widely used for nonlinear dimensionality reduction and manifold learning. While most common implementations rely on both nonlinear enc...
arXiv Machine Learning
2d ago
A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction
arXiv:2604.02535v2 Announce Type: replace Abstract: Dimensionality reduction (DR) involves two longstanding trade-offs. First, preserving local neighborhoods can come at the cost of global structure....