{"total":41,"rows":[{"id":"2311.03757","title":"Manifold learning: what, how, and why","created":"2023-11-07","cited_by_count":68,"area":null},{"id":"2206.01816","title":"From $t$-SNE to UMAP with contrastive learning","created":"2022-06-03","cited_by_count":28,"area":null},{"id":"2210.09879","title":"Unsupervised visualization of image datasets using contrastive learning","created":"2022-10-18","cited_by_count":16,"area":null},{"id":"2308.13837","title":"Class-constrained t-SNE: Combining Data Features and Class Probabilities","created":"2023-08-26","cited_by_count":8,"area":null},{"id":"2305.19043","title":"A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction","created":"2023-05-30","cited_by_count":6,"area":null},{"id":"2503.09101","title":"The Shape of Attraction in UMAP: Exploring the Embedding Forces in Dimensionality Reduction","created":"2025-03-12","cited_by_count":5,"area":null},{"id":"2210.04582","title":"ParaDime: A Framework for Parametric Dimensionality Reduction","created":"2022-10-10","cited_by_count":4,"area":null},{"id":"2401.17675","title":"Convergence analysis of t-SNE as a gradient flow for point cloud on a manifold","created":"2024-01-31","cited_by_count":4,"area":null},{"id":"2410.13063","title":"Large data limits and scaling laws for tSNE","created":"2024-10-16","cited_by_count":4,"area":null},{"id":"2508.13747","title":"DREAMS: Preserving both Local and Global Structure in Dimensionality Reduction","created":"2025-08-19","cited_by_count":4,"area":null},{"id":"2211.09321","title":"Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection","created":"2022-11-17","cited_by_count":3,"area":null},{"id":"2306.11898","title":"Relating tSNE and UMAP to Classical Dimensionality Reduction","created":"2023-06-20","cited_by_count":3,"area":null},{"id":"2311.03087","title":"Persistent Homology for High-dimensional Data Based on Spectral Methods","created":"2023-11-06","cited_by_count":3,"area":null},{"id":"2401.13708","title":"Accelerating hyperbolic t-SNE","created":"2024-01-23","cited_by_count":3,"area":null},{"id":"2403.14813","title":"Curvature Augmented Manifold Embedding and Learning","created":"2024-03-21","cited_by_count":3,"area":null},{"id":"2407.21263","title":"Outlier Detection in Large Radiological Datasets using UMAP","created":"2024-07-31","cited_by_count":3,"area":null},{"id":"2411.15894","title":"Navigating the Effect of Parametrization for Dimensionality Reduction","created":"2024-11-24","cited_by_count":3,"area":null},{"id":"2305.06809","title":"Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets","created":"2023-05-11","cited_by_count":2,"area":null},{"id":"2406.09876","title":"Sailing in high-dimensional spaces: Low-dimensional embeddings through angle preservation","created":"2024-06-14","cited_by_count":2,"area":null},{"id":"2509.03703","title":"EmbedOR: Provable Cluster-Preserving Visualizations with Curvature-Based Stochastic Neighbor Embeddings","created":"2025-09-03","cited_by_count":2,"area":null},{"id":"2306.00357","title":"Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization","created":"2023-06-01","cited_by_count":1,"area":null},{"id":"2308.15513","title":"Navigating Perplexity: A linear relationship with the data set size in t-SNE embeddings","created":"2023-08-29","cited_by_count":1,"area":null},{"id":"2404.10784","title":"Graph Vertex Embeddings: Distance, Regularization and Community Detection","created":"2024-04-09","cited_by_count":1,"area":null},{"id":"2409.11509","title":"FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction","created":"2024-09-17","cited_by_count":1,"area":null},{"id":"2411.10617","title":"Attraction-Repulsion Swarming: A Generalized Framework of t-SNE via Force Normalization and Tunable Interactions","created":"2024-11-15","cited_by_count":1,"area":null},{"id":"2503.03156","title":"Dimensionality reduction for homological stability and global structure preservation","created":"2025-03-05","cited_by_count":1,"area":null},{"id":"2503.23822","title":"Node Embeddings via Neighbor Embeddings","created":"2025-03-31","cited_by_count":1,"area":null},{"id":"2601.20173","title":"MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis","created":"2026-01-28","cited_by_count":1,"area":null},{"id":"2607.26278","title":"FloDR: An invertible dimensionality reduction method based on a normalising flow","created":"2026-07-28","cited_by_count":1,"area":null},{"id":"2207.07218","title":"On the Selection of Tuning Parameters for Patch-Stitching Embedding Methods","created":"2022-07-14","cited_by_count":0,"area":null},{"id":"2309.08077","title":"Supervised Stochastic Neighbor Embedding Using Contrastive Learning","created":"2023-09-15","cited_by_count":0,"area":null},{"id":"2405.00172","title":"Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings","created":"2024-04-30","cited_by_count":0,"area":null},{"id":"2509.03373","title":"Cluster and then Embed: A Modular Approach for Visualization","created":"2025-08-27","cited_by_count":0,"area":null},{"id":"2509.04222","title":"Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation","created":"2025-09-04","cited_by_count":0,"area":null},{"id":"2509.07681","title":"FUnc-SNE: A flexible, Fast, and Unconstrained algorithm for neighbour embeddings","created":"2025-09-09","cited_by_count":0,"area":null},{"id":"2510.15014","title":"The Tree-SNE Tree Exists","created":"2025-10-16","cited_by_count":0,"area":null},{"id":"2604.00485","title":"The Rashomon Effect for Visualizing High-Dimensional Data","created":"2026-04-01","cited_by_count":0,"area":null},{"id":"2604.02535","title":"A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction","created":"2026-04-02","cited_by_count":0,"area":null},{"id":"2605.12451","title":"FuTCR: Future-Targeted Contrast and Repulsion for Continual Panoptic Segmentation","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2606.04451","title":"On Out-of-sample Embedding in UMAP","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2610.01445","title":"ibUMAP: Coherent and Scalable Field Evaluation for UMAP Optimization","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}