论文标题
基于群集的多维缩放嵌入工具用于数据可视化
Cluster-based multidimensional scaling embedding tool for data visualization
论文作者
论文摘要
我们提出了一种新技术，用于可视化称为群集MDS（CL-MDS）的高维数据，该数据解决了降低方法的常见难度：在单个二维可视化中保存原始样本的局部和全局结构。它的算法将众所周知的多维缩放（MDS）工具与$ K $ -MEDOIDS数据聚类技术结合在一起，并实现了二维坐标的层次嵌入，稀疏和估计，以获得其他点。尽管CL-MDS是通常适用的工具，但我们还包括用于原子结构应用的特定食谱。我们将这种方法应用于增加复杂性的非线性数据，而不同的地方层相关，显示出其检索和可视化质量的明显改善。
We present a new technique for visualizing high-dimensional data called cluster MDS (cl-MDS), which addresses a common difficulty of dimensionality reduction methods: preserving both local and global structures of the original sample in a single 2-dimensional visualization. Its algorithm combines the well-known multidimensional scaling (MDS) tool with the $k$-medoids data clustering technique, and enables hierarchical embedding, sparsification and estimation of 2-dimensional coordinates for additional points. While cl-MDS is a generally applicable tool, we also include specific recipes for atomic structure applications. We apply this method to non-linear data of increasing complexity where different layers of locality are relevant, showing a clear improvement in their retrieval and visualization quality.
