# Visualizing Data using GTSNE

arXiv: https://arxiv.org/abs/2108.01301 · HTML: https://mlsanity.com/paper/2108.01301/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)

## Built on by (most cited first, 24 of 24)

- [Beyond Reward: Offline Preference-guided Policy Optimization](https://mlsanity.com/paper/2305.16217/) (2023)
- [Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics](https://mlsanity.com/paper/2202.00259/) (2022)
- [Learning to Foresee: Unveiling the Unlocking Efficiency of On-Policy Distillation](https://mlsanity.com/paper/2605.11739/) (2026)
- [Domain-Specific Bias Filtering for Single Labeled Domain Generalization](https://mlsanity.com/paper/2110.00726/) (2021)
- [Multi-Factors Aware Dual-Attentional Knowledge Tracing](https://mlsanity.com/paper/2108.04741/) (2021)
- [Hierarchical discriminative learning improves visual representations of biomedical microscopy](https://mlsanity.com/paper/2303.01605/) (2023)
- [A Framework for Interpretability in Machine Learning for Medical Imaging](https://mlsanity.com/paper/2310.01685/) (2023)
- [VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees](https://mlsanity.com/paper/2112.00334/) (2021)
- [Disentangling Prosody Representations with Unsupervised Speech Reconstruction](https://mlsanity.com/paper/2212.06972/) (2022)
- [Latent Space Representations of Neural Algorithmic Reasoners](https://mlsanity.com/paper/2307.08874/) (2023)
- [Energy-Based Models for Functional Data using Path Measure Tilting](https://mlsanity.com/paper/2202.01929/) (2022)
- [RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection](https://mlsanity.com/paper/2307.10642/) (2023)
- [Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures](https://mlsanity.com/paper/2302.08647/) (2023)
- [A unified one-shot prosody and speaker conversion system with self-supervised discrete speech units](https://mlsanity.com/paper/2211.06535/) (2022)
- [KnAC: an approach for enhancing cluster analysis with background knowledge and explanations](https://mlsanity.com/paper/2112.08759/) (2021)
- [HistoKT: Cross Knowledge Transfer in Computational Pathology](https://mlsanity.com/paper/2201.11246/) (2022)
- [Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images](https://mlsanity.com/paper/2203.13875/) (2022)
- [Changes-Aware Transformer: Learning Generalized Changes Representation](https://mlsanity.com/paper/2309.13619/) (2023)
- [Interpreting deep embeddings for disease progression clustering](https://mlsanity.com/paper/2307.06060/) (2023)
- [Domain-Adaptive Learning: Unsupervised Adaptation for Histology Images with Improved Loss Function Combination](https://mlsanity.com/paper/2309.17172/) (2023)
- [Adv-4-Adv: Thwarting Changing Adversarial Perturbations via Adversarial Domain Adaptation](https://mlsanity.com/paper/2112.00428/) (2021)
- [Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification](https://mlsanity.com/paper/2201.11511/) (2022)
- [BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis](https://mlsanity.com/paper/2308.08003/) (2023)
- [ATAC-Net: Zoomed view works better for Anomaly Detection](https://mlsanity.com/paper/2406.14398/) (2024)

## Abstract (arXiv)

We present a new method GTSNE to visualize high-dimensional data points in the two dimensional map. The technique is a variation of t-SNE that produces better visualizations by capturing both the local neighborhood structure and the macro structure in the data. This is particularly important for high-dimensional data that lie on continuous low-dimensional manifolds. We illustrate the performance of GTSNE on a wide variety of datasets and compare it the state of art methods, including t-SNE and UMAP. The visualizations produced by GTSNE are better than those produced by the other techniques on almost all of the datasets on the macro structure preservation.