DiRe: A New Dimensionality Reduction Framework for Homological Stability
A new framework called DiRe has been developed by researchers to facilitate force-directed dimensionality reduction while maintaining global structure and homological characteristics in low-dimensional representations. This approach, outlined in a paper on arXiv (arXiv:2503.03156), integrates an initial embedding with graph-based layout optimization and assesses outcomes through measures such as local distortion, context preservation, and persistent homology. Unlike UMAP and tSNE, DiRe emphasizes large-scale geometry quantification via Betti curves and persistence diagrams over local visualization. It is designed for efficiency on contemporary hardware, serving as a valuable resource for data analysis in machine learning and related areas. The paper falls under the category of Computer Science > Machine Learning and includes submission history and references. This research contributes to the ongoing exploration of dimensionality reduction with diverse scientific applications.
Key facts
- DiRe is a force-directed dimensionality reduction framework.
- It preserves global structure and homological features.
- The method combines initial embedding with graph-based layout optimization.
- Evaluation uses local distortion, context preservation, and persistent homology measures.
- DiRe provides a complementary tradeoff to UMAP and tSNE.
- It is designed for embeddings whose large-scale geometry can be quantified via Betti curves and persistence diagrams.
- The paper is available on arXiv with ID 2503.03156.
- The paper is categorized under Computer Science > Machine Learning.
Entities
Institutions
- arXiv