UnProjection: Leveraging Inverse-Projections for Visual Analytics of\n High-Dimensional Data Article Swipe
Mateus Espadoto
,
Gabriel Appleby
,
Ashley Suh
,
Dylan Cashman
,
Mingwei Li
,
Carlos Scheidegger
,
Erik Anderson
,
Remco Chang
,
Alexandru Telea
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.01744
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.01744
Projection techniques are often used to visualize high-dimensional data,\nallowing users to better understand the overall structure of multi-dimensional\nspaces on a 2D screen. Although many such methods exist, comparably little work\nhas been done on generalizable methods of inverse-projection -- the process of\nmapping the projected points, or more generally, the projection space back to\nthe original high-dimensional space. In this paper we present NNInv, a deep\nlearning technique with the ability to approximate the inverse of any\nprojection or mapping. NNInv learns to reconstruct high-dimensional data from\nany arbitrary point on a 2D projection space, giving users the ability to\ninteract with the learned high-dimensional representation in a visual analytics\nsystem. We provide an analysis of the parameter space of NNInv, and offer\nguidance in selecting these parameters. We extend validation of the\neffectiveness of NNInv through a series of quantitative and qualitative\nanalyses. We then demonstrate the method's utility by applying it to three\nvisualization tasks: interactive instance interpolation, classifier agreement,\nand gradient visualization.\n
Related Topics
Concepts
Computer science
Visual analytics
Visualization
Projection (relational algebra)
Interpolation (computer graphics)
Representation (politics)
Artificial intelligence
Inverse
Classifier (UML)
Interactive visual analysis
Analytics
Process (computing)
Data mining
Machine learning
Algorithm
Mathematics
Image (mathematics)
Political science
Politics
Operating system
Geometry
Law
Metadata
- Type
- preprint
- Landing Page
- http://arxiv.org/abs/2111.01744
- https://arxiv.org/pdf/2111.01744
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4286860001
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4286860001Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2111.01744Digital Object Identifier
- Title
-
UnProjection: Leveraging Inverse-Projections for Visual Analytics of\n High-Dimensional DataWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-02Full publication date if available
- Authors
-
Mateus Espadoto, Gabriel Appleby, Ashley Suh, Dylan Cashman, Mingwei Li, Carlos Scheidegger, Erik Anderson, Remco Chang, Alexandru TeleaList of authors in order
- Landing page
-
https://arxiv.org/abs/2111.01744Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2111.01744Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2111.01744Direct OA link when available
- Concepts
-
Computer science, Visual analytics, Visualization, Projection (relational algebra), Interpolation (computer graphics), Representation (politics), Artificial intelligence, Inverse, Classifier (UML), Interactive visual analysis, Analytics, Process (computing), Data mining, Machine learning, Algorithm, Mathematics, Image (mathematics), Political science, Politics, Operating system, Geometry, LawTop 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/W4286860001 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2111.01744 |
| ids.openalex | https://openalex.org/W4286860001 |
| fwci | 0.0 |
| type | preprint |
| title | UnProjection: Leveraging Inverse-Projections for Visual Analytics of\n High-Dimensional Data |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10799 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9994000196456909 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Data Visualization and Analytics |
| topics[1].id | https://openalex.org/T10481 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9884999990463257 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1704 |
| topics[1].subfield.display_name | Computer Graphics and Computer-Aided Design |
| topics[1].display_name | Computer Graphics and Visualization Techniques |
| topics[2].id | https://openalex.org/T10531 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9553999900817871 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Advanced Vision and Imaging |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7467701435089111 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C59732488 |
| concepts[1].level | 3 |
| concepts[1].score | 0.743639349937439 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2528440 |
| concepts[1].display_name | Visual analytics |
| concepts[2].id | https://openalex.org/C36464697 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7049232721328735 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q451553 |
| concepts[2].display_name | Visualization |
| concepts[3].id | https://openalex.org/C57493831 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6812657713890076 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q3134666 |
| concepts[3].display_name | Projection (relational algebra) |
| concepts[4].id | https://openalex.org/C137800194 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5852370262145996 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11713455 |
| concepts[4].display_name | Interpolation (computer graphics) |
| concepts[5].id | https://openalex.org/C2776359362 |
| concepts[5].level | 3 |
| concepts[5].score | 0.509994387626648 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2145286 |
| concepts[5].display_name | Representation (politics) |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.5069911479949951 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C207467116 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4766867160797119 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q4385666 |
| concepts[7].display_name | Inverse |
| concepts[8].id | https://openalex.org/C95623464 |
| concepts[8].level | 2 |
| concepts[8].score | 0.45909982919692993 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1096149 |
| concepts[8].display_name | Classifier (UML) |
| concepts[9].id | https://openalex.org/C99740376 |
| concepts[9].level | 4 |
| concepts[9].score | 0.4418705701828003 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q17092520 |
| concepts[9].display_name | Interactive visual analysis |
| concepts[10].id | https://openalex.org/C79158427 |
| concepts[10].level | 2 |
| concepts[10].score | 0.43433311581611633 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q485396 |
| concepts[10].display_name | Analytics |
| concepts[11].id | https://openalex.org/C98045186 |
| concepts[11].level | 2 |
| concepts[11].score | 0.41951048374176025 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q205663 |
| concepts[11].display_name | Process (computing) |
| concepts[12].id | https://openalex.org/C124101348 |
| concepts[12].level | 1 |
| concepts[12].score | 0.3669241964817047 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[12].display_name | Data mining |
| concepts[13].id | https://openalex.org/C119857082 |
| concepts[13].level | 1 |
| concepts[13].score | 0.3504580855369568 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[13].display_name | Machine learning |
| concepts[14].id | https://openalex.org/C11413529 |
| concepts[14].level | 1 |
| concepts[14].score | 0.3023657202720642 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[14].display_name | Algorithm |
| concepts[15].id | https://openalex.org/C33923547 |
| concepts[15].level | 0 |
| concepts[15].score | 0.16640734672546387 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[15].display_name | Mathematics |
| concepts[16].id | https://openalex.org/C115961682 |
| concepts[16].level | 2 |
| concepts[16].score | 0.13283413648605347 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[16].display_name | Image (mathematics) |
| concepts[17].id | https://openalex.org/C17744445 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q36442 |
| concepts[17].display_name | Political science |
| concepts[18].id | https://openalex.org/C94625758 |
| concepts[18].level | 2 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q7163 |
| concepts[18].display_name | Politics |
| concepts[19].id | https://openalex.org/C111919701 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[19].display_name | Operating system |
| concepts[20].id | https://openalex.org/C2524010 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[20].display_name | Geometry |
| concepts[21].id | https://openalex.org/C199539241 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q7748 |
| concepts[21].display_name | Law |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7467701435089111 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/visual-analytics |
| keywords[1].score | 0.743639349937439 |
| keywords[1].display_name | Visual analytics |
| keywords[2].id | https://openalex.org/keywords/visualization |
| keywords[2].score | 0.7049232721328735 |
| keywords[2].display_name | Visualization |
| keywords[3].id | https://openalex.org/keywords/projection |
| keywords[3].score | 0.6812657713890076 |
| keywords[3].display_name | Projection (relational algebra) |
| keywords[4].id | https://openalex.org/keywords/interpolation |
| keywords[4].score | 0.5852370262145996 |
| keywords[4].display_name | Interpolation (computer graphics) |
| keywords[5].id | https://openalex.org/keywords/representation |
| keywords[5].score | 0.509994387626648 |
| keywords[5].display_name | Representation (politics) |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.5069911479949951 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/inverse |
| keywords[7].score | 0.4766867160797119 |
| keywords[7].display_name | Inverse |
| keywords[8].id | https://openalex.org/keywords/classifier |
| keywords[8].score | 0.45909982919692993 |
| keywords[8].display_name | Classifier (UML) |
| keywords[9].id | https://openalex.org/keywords/interactive-visual-analysis |
| keywords[9].score | 0.4418705701828003 |
| keywords[9].display_name | Interactive visual analysis |
| keywords[10].id | https://openalex.org/keywords/analytics |
| keywords[10].score | 0.43433311581611633 |
| keywords[10].display_name | Analytics |
| keywords[11].id | https://openalex.org/keywords/process |
| keywords[11].score | 0.41951048374176025 |
| keywords[11].display_name | Process (computing) |
| keywords[12].id | https://openalex.org/keywords/data-mining |
| keywords[12].score | 0.3669241964817047 |
| keywords[12].display_name | Data mining |
| keywords[13].id | https://openalex.org/keywords/machine-learning |
| keywords[13].score | 0.3504580855369568 |
| keywords[13].display_name | Machine learning |
| keywords[14].id | https://openalex.org/keywords/algorithm |
| keywords[14].score | 0.3023657202720642 |
| keywords[14].display_name | Algorithm |
| keywords[15].id | https://openalex.org/keywords/mathematics |
| keywords[15].score | 0.16640734672546387 |
| keywords[15].display_name | Mathematics |
| keywords[16].id | https://openalex.org/keywords/image |
| keywords[16].score | 0.13283413648605347 |
| keywords[16].display_name | Image (mathematics) |
| language | |
| locations[0].id | pmh:oai:arXiv.org:2111.01744 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2111.01744 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2111.01744 |
| indexed_in | arxiv |
| authorships[0].author.id | https://openalex.org/A5074525572 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1922-4309 |
| authorships[0].author.display_name | Mateus Espadoto |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Espadoto, Mateus |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5078304037 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-2436-2121 |
| authorships[1].author.display_name | Gabriel Appleby |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Appleby, Gabriel |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5084507075 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6513-8447 |
| authorships[2].author.display_name | Ashley Suh |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Suh, Ashley |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5033915638 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4853-5701 |
| authorships[3].author.display_name | Dylan Cashman |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Cashman, Dylan |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5100425352 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-0457-8091 |
| authorships[4].author.display_name | Mingwei Li |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Li, Mingwei |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5035997603 |
| authorships[5].author.orcid | https://orcid.org/0009-0006-9163-6136 |
| authorships[5].author.display_name | Carlos Scheidegger |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Scheidegger, Carlos |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5022184186 |
| authorships[6].author.orcid | https://orcid.org/0000-0003-3154-9648 |
| authorships[6].author.display_name | Erik Anderson |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Anderson, Erik W |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5089451178 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-6484-6430 |
| authorships[7].author.display_name | Remco Chang |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Chang, Remco |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5024074640 |
| authorships[8].author.orcid | https://orcid.org/0000-0003-0750-0502 |
| authorships[8].author.display_name | Alexandru Telea |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Telea, Alexandru C |
| authorships[8].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://arxiv.org/pdf/2111.01744 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2022-07-25T00:00:00 |
| display_name | UnProjection: Leveraging Inverse-Projections for Visual Analytics of\n High-Dimensional Data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10799 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9994000196456909 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Data Visualization and Analytics |
| related_works | https://openalex.org/W2067923524, https://openalex.org/W1873226627, https://openalex.org/W3004497735, https://openalex.org/W2911982569, https://openalex.org/W2136435231, https://openalex.org/W2138646726, https://openalex.org/W3127482518, https://openalex.org/W2148525144, https://openalex.org/W4302879739, https://openalex.org/W1813318416 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | pmh:oai:arXiv.org:2111.01744 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2111.01744 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2111.01744 |
| primary_location.id | pmh:oai:arXiv.org:2111.01744 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2111.01744 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2111.01744 |
| publication_date | 2021-11-02 |
| publication_year | 2021 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 19, 61, 85, 100, 127 |
| abstract_inverted_index.-- | 37 |
| abstract_inverted_index.2D | 20, 86 |
| abstract_inverted_index.In | 55 |
| abstract_inverted_index.We | 103, 119, 133 |
| abstract_inverted_index.an | 105 |
| abstract_inverted_index.by | 139 |
| abstract_inverted_index.in | 99, 115 |
| abstract_inverted_index.it | 141 |
| abstract_inverted_index.of | 16, 35, 71, 107, 111, 122, 124, 129 |
| abstract_inverted_index.on | 18, 32, 84 |
| abstract_inverted_index.or | 44, 73 |
| abstract_inverted_index.to | 5, 10, 67, 77, 142 |
| abstract_inverted_index.we | 58 |
| abstract_inverted_index.and | 113, 131 |
| abstract_inverted_index.are | 2 |
| abstract_inverted_index.the | 13, 38, 41, 47, 65, 69, 91, 95, 108, 136 |
| abstract_inverted_index.back | 50 |
| abstract_inverted_index.been | 30 |
| abstract_inverted_index.data | 80 |
| abstract_inverted_index.done | 31 |
| abstract_inverted_index.many | 23 |
| abstract_inverted_index.more | 45 |
| abstract_inverted_index.such | 24 |
| abstract_inverted_index.then | 134 |
| abstract_inverted_index.this | 56 |
| abstract_inverted_index.used | 4 |
| abstract_inverted_index.with | 64, 94 |
| abstract_inverted_index.NNInv | 75, 125 |
| abstract_inverted_index.often | 3 |
| abstract_inverted_index.paper | 57 |
| abstract_inverted_index.point | 83 |
| abstract_inverted_index.space | 49, 110 |
| abstract_inverted_index.these | 117 |
| abstract_inverted_index.users | 9, 90 |
| abstract_inverted_index.NNInv, | 60, 112 |
| abstract_inverted_index.better | 11 |
| abstract_inverted_index.exist, | 26 |
| abstract_inverted_index.extend | 120 |
| abstract_inverted_index.giving | 89 |
| abstract_inverted_index.learns | 76 |
| abstract_inverted_index.little | 28 |
| abstract_inverted_index.series | 128 |
| abstract_inverted_index.space, | 88 |
| abstract_inverted_index.space. | 54 |
| abstract_inverted_index.tasks: | 144 |
| abstract_inverted_index.visual | 101 |
| abstract_inverted_index.ability | 66, 92 |
| abstract_inverted_index.inverse | 70 |
| abstract_inverted_index.learned | 96 |
| abstract_inverted_index.methods | 25, 34 |
| abstract_inverted_index.overall | 14 |
| abstract_inverted_index.points, | 43 |
| abstract_inverted_index.present | 59 |
| abstract_inverted_index.process | 39 |
| abstract_inverted_index.provide | 104 |
| abstract_inverted_index.screen. | 21 |
| abstract_inverted_index.through | 126 |
| abstract_inverted_index.to\nthe | 51 |
| abstract_inverted_index.utility | 138 |
| abstract_inverted_index.Although | 22 |
| abstract_inverted_index.analysis | 106 |
| abstract_inverted_index.applying | 140 |
| abstract_inverted_index.gradient | 150 |
| abstract_inverted_index.instance | 146 |
| abstract_inverted_index.mapping. | 74 |
| abstract_inverted_index.method's | 137 |
| abstract_inverted_index.original | 52 |
| abstract_inverted_index.arbitrary | 82 |
| abstract_inverted_index.from\nany | 81 |
| abstract_inverted_index.parameter | 109 |
| abstract_inverted_index.projected | 42 |
| abstract_inverted_index.selecting | 116 |
| abstract_inverted_index.structure | 15 |
| abstract_inverted_index.technique | 63 |
| abstract_inverted_index.visualize | 6 |
| abstract_inverted_index.work\nhas | 29 |
| abstract_inverted_index.Projection | 0 |
| abstract_inverted_index.classifier | 148 |
| abstract_inverted_index.comparably | 27 |
| abstract_inverted_index.generally, | 46 |
| abstract_inverted_index.projection | 48, 87 |
| abstract_inverted_index.techniques | 1 |
| abstract_inverted_index.understand | 12 |
| abstract_inverted_index.validation | 121 |
| abstract_inverted_index.approximate | 68 |
| abstract_inverted_index.demonstrate | 135 |
| abstract_inverted_index.interactive | 145 |
| abstract_inverted_index.of\nmapping | 40 |
| abstract_inverted_index.parameters. | 118 |
| abstract_inverted_index.reconstruct | 78 |
| abstract_inverted_index.quantitative | 130 |
| abstract_inverted_index.to\ninteract | 93 |
| abstract_inverted_index.generalizable | 33 |
| abstract_inverted_index.deep\nlearning | 62 |
| abstract_inverted_index.interpolation, | 147 |
| abstract_inverted_index.representation | 98 |
| abstract_inverted_index.agreement,\nand | 149 |
| abstract_inverted_index.any\nprojection | 72 |
| abstract_inverted_index.data,\nallowing | 8 |
| abstract_inverted_index.offer\nguidance | 114 |
| abstract_inverted_index.high-dimensional | 7, 53, 79, 97 |
| abstract_inverted_index.visualization.\n | 151 |
| abstract_inverted_index.analytics\nsystem. | 102 |
| abstract_inverted_index.inverse-projection | 36 |
| abstract_inverted_index.the\neffectiveness | 123 |
| abstract_inverted_index.three\nvisualization | 143 |
| abstract_inverted_index.qualitative\nanalyses. | 132 |
| abstract_inverted_index.multi-dimensional\nspaces | 17 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.21052959 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |