mlsanityData to 7 Oct 2026

Visualizing Data using GTSNE

arXiv 2108.01301 ↗First posted 3 Aug 2021cs.LG cs.HC math.OCcited by 23

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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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 1 of its 5 references that are in the map. First posted on arXiv 3 Aug 2021.

Builds on

1 of 5 references.

References · 1

  1. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841

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Citing papers per year: 2021: 5; 2022: 7; 2023: 10; 2024: 1.

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  1. 2023Beyond Reward: Offline Preference-guided Policy Optimizationcited by 35
  2. 2022Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semanticscited by 22
  3. 2021Domain-Specific Bias Filtering for Single Labeled Domain Generalizationcited by 18
  4. 2021Multi-Factors Aware Dual-Attentional Knowledge Tracingcited by 12
  5. 2023Hierarchical discriminative learning improves visual representations of biomedical microscopycited by 12
  6. 2023A Framework for Interpretability in Machine Learning for Medical Imagingcited by 9
  7. 2021VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Treescited by 8
  8. 2022Disentangling Prosody Representations with Unsupervised Speech Reconstructioncited by 8
  9. 2023Latent Space Representations of Neural Algorithmic Reasonerscited by 6
  10. 2022Energy-Based Models for Functional Data using Path Measure Tiltingcited by 5
  11. 2023RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detectioncited by 5
  12. 2023Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structurescited by 4
11 more
  1. 2022A unified one-shot prosody and speaker conversion system with self-supervised discrete speech unitscited by 3
  2. 2021KnAC: an approach for enhancing cluster analysis with background knowledge and explanationscited by 2
  3. 2022HistoKT: Cross Knowledge Transfer in Computational Pathologycited by 2
  4. 2022Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy imagescited by 2
  5. 2023Changes-Aware Transformer: Learning Generalized Changes Representationcited by 2
  6. 2023Interpreting deep embeddings for disease progression clusteringcited by 1
  7. 2023Domain-Adaptive Learning: Unsupervised Adaptation for Histology Images with Improved Loss Function Combinationcited by 1
  8. 2021Adv-4-Adv: Thwarting Changing Adversarial Perturbations via Adversarial Domain Adaptationcited by 0
  9. 2022Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classificationcited by 0
  10. 2023BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysiscited by 0
  11. 2024ATAC-Net: Zoomed view works better for Anomaly Detectioncited by 0