<div class="csl-bib-body">
<div class="csl-entry">Matz, G., Verardo, C., & Dittrich, T. (2023). Efficient Learning of Balanced Signature Graphs. In <i>IEEE Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP)</i>. IEEE ICASSP 2023, Rhodos, Greece. https://doi.org/10.1109/ICASSP49357.2023.10095989</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/192717
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dc.description.abstract
The novel concept of signature graphs extends signed graphs by admitting multiple types of partial similarity/agreement or dissimilarity/disagreement. Extending the concept of balancedness to signature graphs yields an explicit and efficient basis for multi-class clustering and classification. Contrary to existing two-stage approaches that consist of graph learning followed by graph clustering, we propose a one-step procedure that directly learns a perfectly clustered graph. We describe the algorithmic constituents for our approach and illustrate its superiority via numerical simulations.
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dc.language.iso
en
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dc.subject
Graph Signal Processing, Clustering Algorithms, Classification Algorithms
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dc.title
Efficient Learning of Balanced Signature Graphs
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
University of Udine, Italy
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dc.relation.isbn
978-1-7281-6327-7
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
IEEE Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP)