论文标题
小队：瘦的随机四重奏MDS改善邻居嵌入T-SNE和UMAP的全球结构保存
SQuadMDS: a lean Stochastic Quartet MDS improving global structure preservation in neighbor embedding like t-SNE and UMAP
论文作者
论文摘要
多维缩放是一个统计过程，旨在将高维数据嵌入较低维空间中。此过程通常用于数据可视化的目的。常见的多维缩放算法倾向于具有较高的计算复杂性，使得它们在大型数据集上不适用。这项工作引入了一种随机，有力的定向方法，用于具有O（n）的时间和空间复杂性，并带有n个数据点。该方法可以与邻居嵌入（例如T-SNE）家族的力量的定向布局结合在一起，以产生保留数据的全局和局部结构的嵌入。实验评估了独立版本产生的嵌入质量及其杂种扩展，既定量和定性，竞争结果都超过了最新方法。代码可在https://github.com/pierrelambert3/squad-mds-and-fitsne-hybrid上找到。
Multidimensional scaling is a statistical process that aims to embed high dimensional data into a lower-dimensional space; this process is often used for the purpose of data visualisation. Common multidimensional scaling algorithms tend to have high computational complexities, making them inapplicable on large data sets. This work introduces a stochastic, force directed approach to multidimensional scaling with a time and space complexity of O(N), with N data points. The method can be combined with force directed layouts of the family of neighbour embedding such as t-SNE, to produce embeddings that preserve both the global and the local structures of the data. Experiments assess the quality of the embeddings produced by the standalone version and its hybrid extension both quantitatively and qualitatively, showing competitive results outperforming state-of-the-art approaches. Codes are available at https://github.com/PierreLambert3/SQuaD-MDS-and-FItSNE-hybrid.
