Strategies for EELS Data Analysis. Introducing UMAP and HDBSCAN for Dimensionality Reduction and Clustering

Hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and uniform manifold approximation and projection (UMAP), two new state-of-the-art algorithms for clustering analysis, and dimensionality reduction, respectively, are proposed for the segmentation of core-loss electro...

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Vydáno v:Microscopy and microanalysis Ročník 28; číslo 1; s. 109 - 122
Hlavní autoři: Blanco-Portals, Javier, Peiró, Francesca, Estradé, Sònia
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, USA Cambridge University Press 01.02.2022
Oxford University Press
Témata:
Algorithms
Classification
Cluster analysis
Clustering
Core-shell particles
Data analysis
Datasets
Electron energy loss spectroscopy
Energy dissipation
Energy loss
Image segmentation
Manganese
Manganese oxides
Nanoparticles
Noise
Software and Instrumentation
Spectroscopy
Spectrum analysis
HDBSCAN
UMAP
dimensionality reduction
clustering
EELS
ISSN:1431-9276, 1435-8115, 1435-8115
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Shrnutí:Hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and uniform manifold approximation and projection (UMAP), two new state-of-the-art algorithms for clustering analysis, and dimensionality reduction, respectively, are proposed for the segmentation of core-loss electron energy loss spectroscopy (EELS) spectrum images. The performances of UMAP and HDBSCAN are systematically compared to the other clustering analysis approaches used in EELS in the literature using a known synthetic dataset. Better results are found for these new approaches. Furthermore, UMAP and HDBSCAN are showcased in a real experimental dataset from a core–shell nanoparticle of iron and manganese oxides, as well as the triple combination nonnegative matrix factorization–UMAP–HDBSCAN. The results obtained indicate how the complementary use of different combinations may be beneficial in a real-case scenario to attain a complete picture, as different algorithms highlight different aspects of the dataset studied.
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ISSN:1431-9276
1435-8115
1435-8115
DOI:10.1017/S1431927621013696