# Multidimensional Scaling on Multiple Input Distance Matrices

arXiv: https://arxiv.org/abs/1605.00286 · HTML: https://mlsanity.com/paper/1605.00286/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2016.

## Built on by (most cited first, 5 of 5)

- [How can classical multidimensional scaling go wrong?](https://mlsanity.com/paper/2110.11430/) (2021)
- [ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE](https://mlsanity.com/paper/2205.11720/) (2022)
- [Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport](https://mlsanity.com/paper/2604.02610/) (2026)
- [Consensus dimension reduction via multi-view learning](https://mlsanity.com/paper/2512.15802/) (2025)
- [HigeNet: A Highly Efficient Modeling for Long Sequence Time Series Prediction in AIOps](https://mlsanity.com/paper/2211.07642/) (2022)

## Abstract (arXiv)

Multidimensional Scaling (MDS) is a classic technique that seeks vectorial representations for data points, given the pairwise distances between them. However, in recent years, data are usually collected from diverse sources or have multiple heterogeneous representations. How to do multidimensional scaling on multiple input distance matrices is still unsolved to our best knowledge. In this paper, we first define this new task formally. Then, we propose a new algorithm called Multi-View Multidimensional Scaling (MVMDS) by considering each input distance matrix as one view. Our algorithm is able to learn the weights of views (i.e., distance matrices) automatically by exploring the consensus information and complementary nature of views. Experimental results on synthetic as well as real datasets demonstrate the effectiveness of MVMDS. We hope that our work encourages a wider consideration in many domains where MDS is needed.