dc.contributor.author | Willsky, Alan S. | |
dc.contributor.author | Tan, Vincent Yan Fu | |
dc.contributor.author | Anandkumar, Animashree | |
dc.date.accessioned | 2012-10-04T13:43:41Z | |
dc.date.available | 2012-10-04T13:43:41Z | |
dc.date.issued | 2010-09 | |
dc.date.submitted | 2010-09 | |
dc.identifier.isbn | 978-1-4244-8215-3 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/73590 | |
dc.description.abstract | The problem of learning forest-structured discrete graphical models from i.i.d. samples is considered. An algorithm based on pruning of the Chow-Liu tree through adaptive thresholding is proposed. It is shown that this algorithm is structurally consistent and the error probability of structure learning decays faster than any polynomial in the number of samples under fixed model size. For the high-dimensional scenario where the size of the model d and the number of edges k scale with the number of samples n, sufficient conditions on (n, d, k) are given for the algorithm to be structurally consistent. In addition, the extremal structures for learning are identified; we prove that the independent (resp. tree) model is the hardest (resp. easiest) to learn using the proposed algorithm in terms of error rates for structure learning. | en_US |
dc.description.sponsorship | United States. Air Force Office of Scientific Research (Grant FA9559-08-1- 1080) | en_US |
dc.description.sponsorship | United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-06-1-0076) | en_US |
dc.description.sponsorship | United States. Army Research Office. Multidisciplinary University Research Initiative (Grant FA9550-06-1-0324) | en_US |
dc.description.sponsorship | Singapore. Agency for Science, Technology and Research | en_US |
dc.language.iso | en_US | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/ALLERTON.2010.5706977 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike 3.0 | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/ | en_US |
dc.source | Other University Web Domain | en_US |
dc.title | Scaling laws for learning high-dimensional Markov forest distributions | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Tan, Vincent Y. F., Animashree Anandkumar, and Alan S. Wi. "Scaling laws for learning high-dimensional Markov forest distributions" 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2010. 712–718. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Laboratory for Information and Decision Systems | en_US |
dc.contributor.mitauthor | Willsky, Alan S. | |
dc.contributor.mitauthor | Tan, Vincent Yan Fu | |
dc.contributor.mitauthor | Anandkumar, Animashree | |
dc.relation.journal | Proceedings of the 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2010 | en_US |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
dspace.orderedauthors | Tan, Vincent Y. F.; Anandkumar, Animashree; Willsky, Alan S. | en |
dc.identifier.orcid | https://orcid.org/0000-0003-0149-5888 | |
mit.license | OPEN_ACCESS_POLICY | en_US |
mit.metadata.status | Complete | |