# NCVis: Noise Contrastive Approach for Scalable Visualization

arXiv: https://arxiv.org/abs/2001.11411 · HTML: https://mlsanity.com/paper/2001.11411/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [From t-SNE to UMAP with contrastive learning](https://mlsanity.com/paper/2206.01816/) (2022)
- [Navigating the Effect of Parametrization for Dimensionality Reduction](https://mlsanity.com/paper/2411.15894/) (2024)
- [Sailing in high-dimensional spaces: Low-dimensional embeddings through angle preservation](https://mlsanity.com/paper/2406.09876/) (2024)
- [Preserving local densities in low-dimensional embeddings](https://mlsanity.com/paper/2301.13732/) (2023)
- [FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction](https://mlsanity.com/paper/2409.11509/) (2024)
- [Node Embeddings via Neighbor Embeddings](https://mlsanity.com/paper/2503.23822/) (2025)
- [NOMAD Projection](https://mlsanity.com/paper/2505.15511/) (2025)
- [The Rashomon Effect for Visualizing High-Dimensional Data](https://mlsanity.com/paper/2604.00485/) (2026)
- [CosMAP: Contrastive Manifold Approximation and Projection for Dimensionality Reduction of Omics and Genealogical Data](https://mlsanity.com/paper/2608.11269/) (2026)

## Abstract (arXiv)

Modern methods for data visualization via dimensionality reduction, such as t-SNE, usually have performance issues that prohibit their application to large amounts of high-dimensional data. In this work, we propose NCVis -- a high-performance dimensionality reduction method built on a sound statistical basis of noise contrastive estimation. We show that NCVis outperforms state-of-the-art techniques in terms of speed while preserving the representation quality of other methods. In particular, the proposed approach successfully proceeds a large dataset of more than 1 million news headlines in several minutes and presents the underlying structure in a human-readable way. Moreover, it provides results consistent with classical methods like t-SNE on more straightforward datasets like images of hand-written digits. We believe that the broader usage of such software can significantly simplify the large-scale data analysis and lower the entry barrier to this area.