Covariate-assisted spectral clustering
| العنوان: | Covariate-assisted spectral clustering |
|---|---|
| المؤلفون: | Joshua T. Vogelstein, Karl Rohe, Norbert Binkiewicz |
| المصدر: | Biometrika |
| بيانات النشر: | Oxford University Press (OUP), 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Statistics and Probability, Connectomics, Fuzzy clustering, General Mathematics, Correlation clustering, Brain graph, Network, Mathematics - Statistics Theory, Machine Learning (stat.ML), Statistics Theory (math.ST), 02 engineering and technology, 01 natural sciences, Machine Learning (cs.LG), Methodology (stat.ME), 010104 statistics & probability, Statistics - Machine Learning, Covariate, Statistics, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, Cluster analysis, Statistics - Methodology, Mathematics, business.industry, Applied Mathematics, Stochastic blockmodel, Pattern recognition, Articles, Mixture model, Node attribute, Agricultural and Biological Sciences (miscellaneous), Spectral clustering, Computer Science - Learning, ComputingMethodologies_PATTERNRECOGNITION, 020201 artificial intelligence & image processing, Artificial intelligence, Laplacian, Statistics, Probability and Uncertainty, General Agricultural and Biological Sciences, business, Canonical correlation |
| الوصف: | Biological and social systems consist of myriad interacting units. The interactions can be represented in the form of a graph or network. Measurements of these graphs can reveal the underlying structure of these interactions, which provides insight into the systems that generated the graphs. Moreover, in applications such as connectomics, social networks, and genomics, graph data are accompanied by contextualizing measures on each node. We utilize these node covariates to help uncover latent communities in a graph, using a modification of spectral clustering. Statistical guarantees are provided under a joint mixture model that we call the node-contextualized stochastic blockmodel, including a bound on the mis-clustering rate. The bound is used to derive conditions for achieving perfect clustering. For most simulated cases, covariate-assisted spectral clustering yields results superior to regularized spectral clustering without node covariates and to an adaptation of canonical correlation analysis. We apply our clustering method to large brain graphs derived from diffusion MRI data, using the node locations or neurological region membership as covariates. In both cases, covariate-assisted spectral clustering yields clusters that are easier to interpret neurologically. Comment: 28 pages, 4 figures, includes substantial changes to theoretical results |
| تدمد: | 1464-3510 0006-3444 |
| DOI: | 10.1093/biomet/asx008 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76b94f89f99e0c43358f5c50085a7369 https://doi.org/10.1093/biomet/asx008 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....76b94f89f99e0c43358f5c50085a7369 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14643510 00063444 |
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| DOI: | 10.1093/biomet/asx008 |