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Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.02.087
Abstract: Abstract Recently, spectral clustering (SC) has been gaining more and more attention due to its excellent performance in unsupervised learning. However, the computational complexity of the SC is high. Also, the adjacency graph matrix of…
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Keywords:
graph embedded;
embedded data;
fast adaptive;
raw data ... See more keywords
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Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-07007-4
Abstract: Subtypes of suicide decedents have not been studied at a population level using linked clinical and public health surveillance records. Identifying suicide subtypes can help facilitate the development and deployment of population-level prevention strategies. This…
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Keywords:
suicide decedent;
embedded clustering;
identifying characterizing;
population level ... See more keywords
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Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3554837
Abstract: Identification of minerals through remote sensing imagery plays an important role in mineral exploration, especially when ground truth data or field work is not accessible. Hyperspectral imaging, which collects information from hundreds of narrow spectral…
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Keywords:
embedded clustering;
multi modal;
clustering dec;
index ... See more keywords
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Published in 2023 at "Seismological Research Letters"
DOI: 10.1785/0220230011
Abstract: Slope disasters, such as landslides and rockfalls, can cause significant safety issues for land and road use in many countries. Therefore, it is important to have effective monitoring and early warning systems in place. Although…
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Keywords:
seismic recordings;
slope;
deep embedded;
cluster analysis ... See more keywords