Distributed data clustering over networks
• Clustering on multiple sources of distributed data. • Non-convex optimization in multi-agent networks with time-varying connectivity; • Leveraging dynamic consensus for diffuse information in the network. • Consensus-based Expectation-Maximization applied to Gaussian mixture models.
Autor*in: |
Altilio, Rosa [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2019 |
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Umfang: |
18 |
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Übergeordnetes Werk: |
Enthalten in: Association between dopa decarboxylase gene variants and borderline personality disorder - Mobascher, Arian ELSEVIER, 2014, the journal of the Pattern Recognition Society, Amsterdam |
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Übergeordnetes Werk: |
volume:93 ; year:2019 ; pages:603-620 ; extent:18 |
Links: |
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DOI / URN: |
10.1016/j.patcog.2019.04.021 |
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• Clustering on multiple sources of distributed data. • Non-convex optimization in multi-agent networks with time-varying connectivity; • Leveraging dynamic consensus for diffuse information in the network. • Consensus-based Expectation-Maximization applied to Gaussian mixture models. |
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• Clustering on multiple sources of distributed data. • Non-convex optimization in multi-agent networks with time-varying connectivity; • Leveraging dynamic consensus for diffuse information in the network. • Consensus-based Expectation-Maximization applied to Gaussian mixture models. |
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• Clustering on multiple sources of distributed data. • Non-convex optimization in multi-agent networks with time-varying connectivity; • Leveraging dynamic consensus for diffuse information in the network. • Consensus-based Expectation-Maximization applied to Gaussian mixture models. |
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