Abstrakti
A main task in data analysis is to organize data points into coherent groups or clusters. The stochastic block model is a probabilistic model for the cluster structure. This model prescribes different probabilities for the presence of edges within a cluster and between different clusters. We assume that the cluster assignments are known for at least one data point in each cluster. In such a partially labeled stochastic block model, clustering amounts to estimating the cluster assignments of the remaining data points. We study total variation minimization as a method for this clustering task. We implement the resulting clustering algorithm as a highly scalable message passing protocol. We also provide a condition on the model parameters such that total variation minimization allows for accurate clustering.
Alkuperäiskieli | Englanti |
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Otsikko | Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 |
Toimittajat | Michael B. Matthews |
Kustantaja | IEEE |
Sivut | 731-735 |
Sivumäärä | 5 |
ISBN (elektroninen) | 9780738131269 |
DOI - pysyväislinkit | |
Tila | Julkaistu - 1 marrask. 2020 |
OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
Tapahtuma | Asilomar Conference on Signals, Systems & Computers - Pacific Grove, Yhdysvallat Kesto: 1 marrask. 2020 → 5 marrask. 2020 Konferenssinumero: 54 |
Julkaisusarja
Nimi | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Kustantaja | IEEE Computer Society |
Vuosikerta | 2020-November |
ISSN (painettu) | 1058-6393 |
Conference
Conference | Asilomar Conference on Signals, Systems & Computers |
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Lyhennettä | ACSSC |
Maa/Alue | Yhdysvallat |
Kaupunki | Pacific Grove |
Ajanjakso | 01/11/2020 → 05/11/2020 |