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Multidimensional scaling is a statistical process that aims to embed highdimensional data into a lower-dimensional space; this process is often used forthe purpose of data visualisation. Common multidimensional scaling algorithmstend to have high computational complexities, making them inapplicable on largedata sets. This work introduces a stochastic, force directed approach tomultidimensional scaling with a time and space complexity of O(N), with N datapoints. The method can be combined with force directed layouts of the family ofneighbour embedding such as t-SNE, to produce embeddings that preserve both theglobal and the local structures of the data. Experiments assess the quality ofthe embeddings produced by the standalone version and its hybrid extension bothquantitatively and qualitatively, showing competitive results outperformingstate-of-the-art approaches. Codes are available athttps://github.com/PierreLambert3/SQuaD-MDS-and-FItSNE-hybrid.

SQuadMDS: a lean Stochastic Quartet MDS improving global structure preservation in neighbor embedding like t-SNE and UMAP