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UID:656@lincs.fr
DTSTART;TZID=Europe/Paris:20210915T140000
DTEND;TZID=Europe/Paris:20210915T150000
DTSTAMP:20210916T105918Z
URL:https://www.lincs.fr/events/higher-order-spectral-clustering-for-geome
 tric-graphs/
SUMMARY:Higher-Order Spectral Clustering for Geometric Graphs
DESCRIPTION:This work is devoted to clustering geometric graphs. It appears
 that the standard spectral clustering is often not effective for geometric
 graphs. We present an effective generalization\, which we call higher-order
 spectral clustering. It resembles in concept the classical spectral
 clustering method but uses for partitioning the eigenvector associated with
 a higher-order eigenvalue. We establish the weak consistency of this
 algorithm for a wide class of geometric graphs which we call Soft Geometric
 Block Model. A small adjustment of the algorithm provides strong
 consistency. We also show that our method is effective in numerical
 experiments even for graphs of modest size.\n&nbsp\;\nThis is a joint work
 with A. Bobu and M. Dreveton\, done in the framework of Inria - Nokia Bell
 Labs and recently appeared in JFAA\, 27:22\,
 2021.\nhttps://link.springer.com/article/10.1007/s00041-021-09825-2\n&nbsp\
 ;\n**Here the link to the presentation's slides: HOSC_LINCSPresent
CATEGORIES:Seminars,Youtube
LOCATION:Zoom + LINCS\, 23 avenue d'Italie\, Paris\, 75013\, France
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TZID:Europe/Paris
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DTSTART:20210328T030000
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