Learning partial correlation graphs and graphical models by covariance queries
| Autor: | Lugosi, G., Truszkowski, J., Velona, V., Piotr Zwiernik |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Statistics - Machine Learning Computer Science - Data Structures and Algorithms FOS: Mathematics MathematicsofComputing_NUMERICALANALYSIS Data Structures and Algorithms (cs.DS) Machine Learning (stat.ML) Mathematics - Statistics Theory Statistics Theory (math.ST) 62H99 Computer Science::Databases Machine Learning (cs.LG) |
| Zdroj: | Scopus-Elsevier |
| Popis: | We study the problem of recovering the structure underlying large Gaussian graphical models or, more generally, partial correlation graphs. In high-dimensional problems it is often too costly to store the entire sample covariance matrix. We propose a new input model in which one can query single entries of the covariance matrix. We prove that it is possible to recover the support of the inverse covariance matrix with low query and computational complexity. Our algorithms work in a regime when this support is represented by tree-like graphs and, more generally, for graphs of small treewidth. Our results demonstrate that for large classes of graphs, the structure of the corresponding partial correlation graphs can be determined much faster than even computing the empirical covariance matrix. The title of the paper was changed. The previous title was 'Structure learning in graphical models by covariance queries'. Other minor changes suggested by referees were also implemented |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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