Cluster-based multidimensional scaling embedding tool for data visualization

Patricia Hernandez Leon*, Miguel A. Caro

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

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.

Original languageEnglish
Article number066004
Number of pages20
JournalPhysica Scripta
Volume99
Issue number6
DOIs
Publication statusPublished - 9 May 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • cluster MDS
  • data visualization
  • dimensionality reduction
  • embedding algorithms
  • machine learning

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