# Optimizing Multidimensional Scaling in Gini Metric Spaces

arXiv: https://arxiv.org/abs/2605.25124 · HTML: https://mlsanity.com/paper/2605.25124/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 24 May 2026.

## Builds on (most cited first, 5 of 5)

- [Continuous Multidimensional Scaling](https://mlsanity.com/paper/2402.04436/) (2024)
- [Outlier Detection for Robust Multi-dimensional Scaling](https://mlsanity.com/paper/1802.02341/) (2018)
- [Robust Multi-Dimensional Scaling via Accelerated Alternating Projections](https://mlsanity.com/paper/2501.02208/) (2025)
- [Grouped feature screening for ultrahigh-dimensional classification via Gini distance correlation](https://mlsanity.com/paper/2304.08605/) (2023)
- [KNN and K-means in Gini Prametric Spaces](https://mlsanity.com/paper/2501.18028/) (2025)

## Abstract (arXiv)

The Gini Multidimensional Scaling (Gini MDS) framework extends the Euclidean multidimensional scaling. We introduce a Gini pseudo-distance based on values and their ranks that depends on a fine-tunable hyperparameter. This pseudo-distance allows flexible exploration of latent configurations, enabling embeddings that best match observed dissimilarities. The Gini MDS is shown to be robust to noise and outliers, making it well-suited for real-world applications. We provide experiments on 16 UCI datasets with outliers and on MNIST images with noise to show that the Gini MDS outperforms the Euclidean MDS on noisy data. Finally, a tensor-based implementation in textttPyTorch provides GPU acceleration and efficient computation compared to the standard MDS of the textttsklearn library.