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Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs

We consider a problem of grouping multiple graphs into several clusters using singular value thesholding and non-negative factorization. We derive a model selection information criterion to estimate the number of clusters. We demonstrate our approach using "Swimmer data set" as well as simulated data set, and compare its performance with two standard clustering algorithms.

preprint2014arXivOpen access
Nam H. LeeI-Jeng WangYoungser ParkCare E. PriebeMichael Rosen
Machine Learning
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Open access5 authors1 topic
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Nam H. LeeI-Jeng WangYoungser ParkCare E. PriebeMichael Rosen

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