Graphical Model Selection for Gaussian Conditional Random Fields in the Presence of Latent Variables Article Swipe
Benjamin Frot
,
Luke Jostins
,
Gil McVean
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.6084/m9.figshare.5885179
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.6084/m9.figshare.5885179
We consider the problem of learning a conditional Gaussian graphical model in the presence of latent variables. Building on recent advances in this field, we suggest a method that decomposes the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive convergence bounds for this estimator and show that it is well-behaved in the high-dimensional regime as well as “sparsistent” (i.e., capable of recovering the graph structure). We then show how proximal gradient algorithms and semi-definite programming techniques can be employed to fit the model to thousands of variables. Through extensive simulations, we illustrate the conditions required for identifiability and show that there is a wide range of situations in which this model performs significantly better than its counterparts, for example, by accommodating more latent variables. Finally, the suggested method is applied to two datasets comprising individual level data on genetic variants and metabolites levels. We show our results replicate better than alternative approaches and show enriched biological signal. Supplementary materials for this article are available online.
Related Topics
Concepts
Metadata
- Type
- dataset
- Language
- en
- Landing Page
- https://doi.org/10.6084/m9.figshare.5885179
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394559631
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4394559631Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.6084/m9.figshare.5885179Digital Object Identifier
- Title
-
Graphical Model Selection for Gaussian Conditional Random Fields in the Presence of Latent VariablesWork title
- Type
-
datasetOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-01-01Full publication date if available
- Authors
-
Benjamin Frot, Luke Jostins, Gil McVeanList of authors in order
- Landing page
-
https://doi.org/10.6084/m9.figshare.5885179Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.6084/m9.figshare.5885179Direct OA link when available
- Concepts
-
Graphical model, Conditional random field, Latent variable, Gaussian, Conditional dependence, Latent class model, Selection (genetic algorithm), Computer science, Econometrics, Statistical physics, Statistics, Mathematics, Artificial intelligence, Machine learning, Physics, Chemistry, Computational chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4394559631 |
|---|---|
| doi | https://doi.org/10.6084/m9.figshare.5885179 |
| ids.doi | https://doi.org/10.6084/m9.figshare.5885179 |
| ids.openalex | https://openalex.org/W4394559631 |
| fwci | |
| type | dataset |
| title | Graphical Model Selection for Gaussian Conditional Random Fields in the Presence of Latent Variables |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12814 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9656000137329102 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Gaussian Processes and Bayesian Inference |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C155846161 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7172442078590393 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1143367 |
| concepts[0].display_name | Graphical model |
| concepts[1].id | https://openalex.org/C152565575 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6533522009849548 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1124538 |
| concepts[1].display_name | Conditional random field |
| concepts[2].id | https://openalex.org/C51167844 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6112176775932312 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q4422623 |
| concepts[2].display_name | Latent variable |
| concepts[3].id | https://openalex.org/C163716315 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5551952719688416 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q901177 |
| concepts[3].display_name | Gaussian |
| concepts[4].id | https://openalex.org/C174920663 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5313271880149841 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q5159256 |
| concepts[4].display_name | Conditional dependence |
| concepts[5].id | https://openalex.org/C70727504 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5185911655426025 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1806878 |
| concepts[5].display_name | Latent class model |
| concepts[6].id | https://openalex.org/C81917197 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5104762315750122 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q628760 |
| concepts[6].display_name | Selection (genetic algorithm) |
| concepts[7].id | https://openalex.org/C41008148 |
| concepts[7].level | 0 |
| concepts[7].score | 0.4359460771083832 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[7].display_name | Computer science |
| concepts[8].id | https://openalex.org/C149782125 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4076603651046753 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[8].display_name | Econometrics |
| concepts[9].id | https://openalex.org/C121864883 |
| concepts[9].level | 1 |
| concepts[9].score | 0.40019112825393677 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q677916 |
| concepts[9].display_name | Statistical physics |
| concepts[10].id | https://openalex.org/C105795698 |
| concepts[10].level | 1 |
| concepts[10].score | 0.32881855964660645 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[10].display_name | Statistics |
| concepts[11].id | https://openalex.org/C33923547 |
| concepts[11].level | 0 |
| concepts[11].score | 0.32840532064437866 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[11].display_name | Mathematics |
| concepts[12].id | https://openalex.org/C154945302 |
| concepts[12].level | 1 |
| concepts[12].score | 0.2796780467033386 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[12].display_name | Artificial intelligence |
| concepts[13].id | https://openalex.org/C119857082 |
| concepts[13].level | 1 |
| concepts[13].score | 0.2588646411895752 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[13].display_name | Machine learning |
| concepts[14].id | https://openalex.org/C121332964 |
| concepts[14].level | 0 |
| concepts[14].score | 0.11216920614242554 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[14].display_name | Physics |
| concepts[15].id | https://openalex.org/C185592680 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0902262032032013 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[15].display_name | Chemistry |
| concepts[16].id | https://openalex.org/C147597530 |
| concepts[16].level | 1 |
| concepts[16].score | 0.04878857731819153 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q369472 |
| concepts[16].display_name | Computational chemistry |
| keywords[0].id | https://openalex.org/keywords/graphical-model |
| keywords[0].score | 0.7172442078590393 |
| keywords[0].display_name | Graphical model |
| keywords[1].id | https://openalex.org/keywords/conditional-random-field |
| keywords[1].score | 0.6533522009849548 |
| keywords[1].display_name | Conditional random field |
| keywords[2].id | https://openalex.org/keywords/latent-variable |
| keywords[2].score | 0.6112176775932312 |
| keywords[2].display_name | Latent variable |
| keywords[3].id | https://openalex.org/keywords/gaussian |
| keywords[3].score | 0.5551952719688416 |
| keywords[3].display_name | Gaussian |
| keywords[4].id | https://openalex.org/keywords/conditional-dependence |
| keywords[4].score | 0.5313271880149841 |
| keywords[4].display_name | Conditional dependence |
| keywords[5].id | https://openalex.org/keywords/latent-class-model |
| keywords[5].score | 0.5185911655426025 |
| keywords[5].display_name | Latent class model |
| keywords[6].id | https://openalex.org/keywords/selection |
| keywords[6].score | 0.5104762315750122 |
| keywords[6].display_name | Selection (genetic algorithm) |
| keywords[7].id | https://openalex.org/keywords/computer-science |
| keywords[7].score | 0.4359460771083832 |
| keywords[7].display_name | Computer science |
| keywords[8].id | https://openalex.org/keywords/econometrics |
| keywords[8].score | 0.4076603651046753 |
| keywords[8].display_name | Econometrics |
| keywords[9].id | https://openalex.org/keywords/statistical-physics |
| keywords[9].score | 0.40019112825393677 |
| keywords[9].display_name | Statistical physics |
| keywords[10].id | https://openalex.org/keywords/statistics |
| keywords[10].score | 0.32881855964660645 |
| keywords[10].display_name | Statistics |
| keywords[11].id | https://openalex.org/keywords/mathematics |
| keywords[11].score | 0.32840532064437866 |
| keywords[11].display_name | Mathematics |
| keywords[12].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[12].score | 0.2796780467033386 |
| keywords[12].display_name | Artificial intelligence |
| keywords[13].id | https://openalex.org/keywords/machine-learning |
| keywords[13].score | 0.2588646411895752 |
| keywords[13].display_name | Machine learning |
| keywords[14].id | https://openalex.org/keywords/physics |
| keywords[14].score | 0.11216920614242554 |
| keywords[14].display_name | Physics |
| keywords[15].id | https://openalex.org/keywords/chemistry |
| keywords[15].score | 0.0902262032032013 |
| keywords[15].display_name | Chemistry |
| keywords[16].id | https://openalex.org/keywords/computational-chemistry |
| keywords[16].score | 0.04878857731819153 |
| keywords[16].display_name | Computational chemistry |
| language | en |
| locations[0].id | doi:10.6084/m9.figshare.5885179 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | |
| locations[0].raw_type | dataset |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | False |
| locations[0].is_published | |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.6084/m9.figshare.5885179 |
| indexed_in | datacite |
| authorships[0].author.id | https://openalex.org/A5028359829 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Benjamin Frot |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Benjamin Frot |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5049890044 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Luke Jostins |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Luke Jostins |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5000481707 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5012-4162 |
| authorships[2].author.display_name | Gil McVean |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Gilean McVean |
| authorships[2].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.6084/m9.figshare.5885179 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Graphical Model Selection for Gaussian Conditional Random Fields in the Presence of Latent Variables |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12814 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9656000137329102 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Gaussian Processes and Bayesian Inference |
| related_works | https://openalex.org/W2043914344, https://openalex.org/W2950342388, https://openalex.org/W2088024891, https://openalex.org/W1603253275, https://openalex.org/W2129999749, https://openalex.org/W4302218341, https://openalex.org/W1966538595, https://openalex.org/W2006196395, https://openalex.org/W1501016332, https://openalex.org/W2763292376 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.6084/m9.figshare.5885179 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | |
| best_oa_location.raw_type | dataset |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.6084/m9.figshare.5885179 |
| primary_location.id | doi:10.6084/m9.figshare.5885179 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | |
| primary_location.raw_type | dataset |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.6084/m9.figshare.5885179 |
| publication_date | 2018-01-01 |
| publication_year | 2018 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 6, 26, 33, 42, 45, 113 |
| abstract_inverted_index.We | 0, 48, 76, 154 |
| abstract_inverted_index.as | 65, 67 |
| abstract_inverted_index.be | 88 |
| abstract_inverted_index.by | 130 |
| abstract_inverted_index.in | 11, 21, 61, 118 |
| abstract_inverted_index.is | 59, 112, 139 |
| abstract_inverted_index.it | 58 |
| abstract_inverted_index.of | 4, 14, 32, 41, 71, 96, 116 |
| abstract_inverted_index.on | 18, 148 |
| abstract_inverted_index.to | 90, 94, 141 |
| abstract_inverted_index.we | 24, 101 |
| abstract_inverted_index.and | 44, 55, 83, 108, 151, 163 |
| abstract_inverted_index.are | 173 |
| abstract_inverted_index.can | 87 |
| abstract_inverted_index.fit | 91 |
| abstract_inverted_index.for | 52, 106, 128, 170 |
| abstract_inverted_index.how | 79 |
| abstract_inverted_index.its | 126 |
| abstract_inverted_index.our | 156 |
| abstract_inverted_index.sum | 40 |
| abstract_inverted_index.the | 2, 12, 30, 39, 62, 73, 92, 103, 136 |
| abstract_inverted_index.two | 142 |
| abstract_inverted_index.data | 147 |
| abstract_inverted_index.into | 38 |
| abstract_inverted_index.more | 132 |
| abstract_inverted_index.show | 56, 78, 109, 155, 164 |
| abstract_inverted_index.than | 125, 160 |
| abstract_inverted_index.that | 28, 57, 110 |
| abstract_inverted_index.then | 77 |
| abstract_inverted_index.this | 22, 53, 120, 171 |
| abstract_inverted_index.well | 66 |
| abstract_inverted_index.wide | 114 |
| abstract_inverted_index.field | 37 |
| abstract_inverted_index.graph | 74 |
| abstract_inverted_index.level | 146 |
| abstract_inverted_index.model | 10, 93, 121 |
| abstract_inverted_index.range | 115 |
| abstract_inverted_index.there | 111 |
| abstract_inverted_index.which | 119 |
| abstract_inverted_index.(i.e., | 69 |
| abstract_inverted_index.Markov | 35 |
| abstract_inverted_index.better | 124, 159 |
| abstract_inverted_index.bounds | 51 |
| abstract_inverted_index.derive | 49 |
| abstract_inverted_index.field, | 23 |
| abstract_inverted_index.latent | 15, 133 |
| abstract_inverted_index.method | 27, 138 |
| abstract_inverted_index.random | 36 |
| abstract_inverted_index.recent | 19 |
| abstract_inverted_index.regime | 64 |
| abstract_inverted_index.sparse | 43 |
| abstract_inverted_index.Through | 98 |
| abstract_inverted_index.applied | 140 |
| abstract_inverted_index.article | 172 |
| abstract_inverted_index.capable | 70 |
| abstract_inverted_index.genetic | 149 |
| abstract_inverted_index.levels. | 153 |
| abstract_inverted_index.matrix. | 47 |
| abstract_inverted_index.online. | 175 |
| abstract_inverted_index.problem | 3 |
| abstract_inverted_index.results | 157 |
| abstract_inverted_index.signal. | 167 |
| abstract_inverted_index.suggest | 25 |
| abstract_inverted_index.Building | 17 |
| abstract_inverted_index.Finally, | 135 |
| abstract_inverted_index.Gaussian | 8 |
| abstract_inverted_index.advances | 20 |
| abstract_inverted_index.consider | 1 |
| abstract_inverted_index.datasets | 143 |
| abstract_inverted_index.employed | 89 |
| abstract_inverted_index.enriched | 165 |
| abstract_inverted_index.example, | 129 |
| abstract_inverted_index.gradient | 81 |
| abstract_inverted_index.learning | 5 |
| abstract_inverted_index.low-rank | 46 |
| abstract_inverted_index.performs | 122 |
| abstract_inverted_index.presence | 13 |
| abstract_inverted_index.proximal | 80 |
| abstract_inverted_index.required | 105 |
| abstract_inverted_index.variants | 150 |
| abstract_inverted_index.available | 174 |
| abstract_inverted_index.estimator | 54 |
| abstract_inverted_index.extensive | 99 |
| abstract_inverted_index.graphical | 9 |
| abstract_inverted_index.materials | 169 |
| abstract_inverted_index.replicate | 158 |
| abstract_inverted_index.suggested | 137 |
| abstract_inverted_index.thousands | 95 |
| abstract_inverted_index.algorithms | 82 |
| abstract_inverted_index.approaches | 162 |
| abstract_inverted_index.biological | 166 |
| abstract_inverted_index.comprising | 144 |
| abstract_inverted_index.conditions | 104 |
| abstract_inverted_index.decomposes | 29 |
| abstract_inverted_index.illustrate | 102 |
| abstract_inverted_index.individual | 145 |
| abstract_inverted_index.parameters | 31 |
| abstract_inverted_index.recovering | 72 |
| abstract_inverted_index.situations | 117 |
| abstract_inverted_index.techniques | 86 |
| abstract_inverted_index.variables. | 16, 97, 134 |
| abstract_inverted_index.alternative | 161 |
| abstract_inverted_index.conditional | 7, 34 |
| abstract_inverted_index.convergence | 50 |
| abstract_inverted_index.metabolites | 152 |
| abstract_inverted_index.programming | 85 |
| abstract_inverted_index.structure). | 75 |
| abstract_inverted_index.simulations, | 100 |
| abstract_inverted_index.well-behaved | 60 |
| abstract_inverted_index.Supplementary | 168 |
| abstract_inverted_index.accommodating | 131 |
| abstract_inverted_index.counterparts, | 127 |
| abstract_inverted_index.semi-definite | 84 |
| abstract_inverted_index.significantly | 123 |
| abstract_inverted_index.identifiability | 107 |
| abstract_inverted_index.high-dimensional | 63 |
| abstract_inverted_index.“sparsistent” | 68 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |