Cluster-based multidimensional scaling embedding tool for data visualization
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arXiv
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866929355509202944 |
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| author | Hernández-León, Patricia Caro, Miguel A. |
| author_facet | Hernández-León, Patricia Caro, Miguel A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_06614 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Cluster-based multidimensional scaling embedding tool for data visualization Hernández-León, Patricia Caro, Miguel A. Graphics Materials Science 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. |
| title | Cluster-based multidimensional scaling embedding tool for data visualization |
| topic | Graphics Materials Science |
| url | https://arxiv.org/abs/2209.06614 |