2016
DOI: 10.1016/j.sab.2016.02.003
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Application of Graph Theory to unsupervised classification of materials by Laser-Induced Breakdown Spectroscopy

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Cited by 21 publications
(11 citation statements)
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“…The toners' classification was obtained using a new statistical method, the graph clustering method proposed by Grifoni et al 38 The technique was recently used for the classification of LIBS spectra of sedimentary and igneous rocks 39 . According to their similarity, the graph clustering method classifies the LIBS spectra by defining a distance between the spectra.…”
Section: Methodsmentioning
confidence: 99%
“…The toners' classification was obtained using a new statistical method, the graph clustering method proposed by Grifoni et al 38 The technique was recently used for the classification of LIBS spectra of sedimentary and igneous rocks 39 . According to their similarity, the graph clustering method classifies the LIBS spectra by defining a distance between the spectra.…”
Section: Methodsmentioning
confidence: 99%
“…Common supervised machine learning algorithms that have been used in LIBS studies comprise Principal Component Analysis (PCA) [ 31 ] and k-Means Clustering [ 32 ], while there have been some works that employ some neural network architectures for unsupervised learning, such as Self-Organizing Maps (SOMs) [ 33 ] and Restricted Boltzmann Machines (RBMs) [ 34 ]. Less commonly, graph theory-based algorithms have been also used for the treatment of LIBS spectra in an unsupervised manner, with impressive results [ 35 ].…”
Section: Chemometrics and Machine/deep Learning For Libsmentioning
confidence: 99%
“…BC) in Sardinia (see figure 8). A blind clustering analysis of the LIBS spectra based on Graph Theory [122] helped in determining the provenance of raw materials employed for the artefacts, indicating the compatibility of all analysed statue samples with geological outcrops from Santa Caterina di Pittinuri area. The potential of LIBS for underwater analysis of marbles has been demonstrated in 2005 by Lazic et al [123] and by Guirado et al in [124].…”
Section: Libs Analysis Of Geological Materialsmentioning
confidence: 99%