# Deep Learning Multidimensional Projections

arXiv: https://arxiv.org/abs/1902.07958 · HTML: https://mlsanity.com/paper/1902.07958/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2019.

## Built on by (most cited first, 14 of 14)

- [UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data](https://mlsanity.com/paper/2111.01744/) (2021)
- [Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization](https://mlsanity.com/paper/2307.11770/) (2023)
- [DeepSI: Interactive Deep Learning for Semantic Interaction](https://mlsanity.com/paper/2305.18357/) (2023)
- [Deep Neural Network for DrawiNg Networks, (DNN)^2](https://mlsanity.com/paper/2108.03632/) (2021)
- [HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters](https://mlsanity.com/paper/2106.13777/) (2021)
- [ShaRP: Shape-Regularized Multidimensional Projections](https://mlsanity.com/paper/2306.00554/) (2023)
- [Evaluating Autoencoders for Parametric and Invertible Multidimensional Projections](https://mlsanity.com/paper/2504.16831/) (2025)
- [Deep Recursive Embedding for High-Dimensional Data](https://mlsanity.com/paper/2104.05171/) (2021)
- [Deep Recursive Embedding for High-Dimensional Data](https://mlsanity.com/paper/2111.00622/) (2021)
- [DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy](https://mlsanity.com/paper/2508.12145/) (2025)
- [LCIP: Loss-Controlled Inverse Projection of High-Dimensional Image Data](https://mlsanity.com/paper/2602.11141/) (2026)
- [ShapDBM: Exploring Decision Boundary Maps in Shapley Space](https://mlsanity.com/paper/2603.22235/) (2026)
- [Local Neighborhood Instability in Parametric Projections: Quantitative and Visual Analysis](https://mlsanity.com/paper/2604.21617/) (2026)
- [FADEx: Feature Attribution and Distortion-based Explanation of Dimensionality Reduction](https://mlsanity.com/paper/2607.27463/) (2026)

## Abstract (arXiv)

Dimensionality reduction methods, also known as projections, are frequently used for exploring multidimensional data in machine learning, data science, and information visualization. Among these, t-SNE and its variants have become very popular for their ability to visually separate distinct data clusters. However, such methods are computationally expensive for large datasets, suffer from stability problems, and cannot directly handle out-of-sample data. We propose a learning approach to construct such projections. We train a deep neural network based on a collection of samples from a given data universe, and their corresponding projections, and next use the network to infer projections of data from the same, or similar, universes. Our approach generates projections with similar characteristics as the learned ones, is computationally two to three orders of magnitude faster than SNE-class methods, has no complex-to-set user parameters, handles out-of-sample data in a stable manner, and can be used to learn any projection technique. We demonstrate our proposal on several real-world high dimensional datasets from machine learning.