DCMDS-RV: density-concentrated multi-dimensional scaling for relation visualization View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Bo Wu, James S. Smith, Bogdan M. Wilamowski, R. M. Nelms

ABSTRACT

This paper proposes a novel unsupervised multi-dimensional scaling (MDS) method to visualize high-dimensional data and their relations in a low-dimensional (e.g., 2D) space. Different from traditional MDS approaches where the main purpose is to embed high-dimensional data into a low-dimensional space, this study aims to both embed data into a low-dimensional space and reveal data relations, thus providing better visualization as graph. By taking into account the density relationships inherent in data, this paper proposes a new density-concentrated multi-dimensional scaling algorithm DCMDS-RV to perform visualization of high-dimensional data and their relations. One benefit of the proposed DCMDS-RV algorithm is the ability to embed data more accurately than traditional MDS techniques by using second-order gradient optimization instead of first-order gradient. A key advantage of the presented DCMDS-RV algorithm is the capability to show relations as categorical information. In the resulting embedding, data are compact in clusters. The results demonstrate that the proposed DCMDS-RV algorithm outperforms conventional MDS methods regarding Kruskal stress factor and ACC value. The relations between data as graph are clearly viewed as well. More... »

PAGES

341-357

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12650-018-0532-0

DOI

http://dx.doi.org/10.1007/s12650-018-0532-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110133955


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Auburn University", 
          "id": "https://www.grid.ac/institutes/grid.252546.2", 
          "name": [
            "Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wu", 
        "givenName": "Bo", 
        "id": "sg:person.010606140353.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010606140353.42"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Auburn University", 
          "id": "https://www.grid.ac/institutes/grid.252546.2", 
          "name": [
            "Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Smith", 
        "givenName": "James S.", 
        "id": "sg:person.013171041706.95", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013171041706.95"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Auburn University", 
          "id": "https://www.grid.ac/institutes/grid.252546.2", 
          "name": [
            "Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wilamowski", 
        "givenName": "Bogdan M.", 
        "id": "sg:person.01074224145.90", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01074224145.90"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Auburn University", 
          "id": "https://www.grid.ac/institutes/grid.252546.2", 
          "name": [
            "Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nelms", 
        "givenName": "R. M.", 
        "id": "sg:person.010007435523.13", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010007435523.13"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s12650-011-0084-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006457820", 
          "https://doi.org/10.1007/s12650-011-0084-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf03181472", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007626457", 
          "https://doi.org/10.1007/bf03181472"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf03181472", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007626457", 
          "https://doi.org/10.1007/bf03181472"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-8-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009905742", 
          "https://doi.org/10.1186/1471-2105-8-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1031596100", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014613620"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12650-014-0246-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026544049", 
          "https://doi.org/10.1007/s12650-014-0246-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1242072", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027692695"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.290.5500.2319", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028334489"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-1904-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031639131", 
          "https://doi.org/10.1007/978-1-4757-1904-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-1904-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031639131", 
          "https://doi.org/10.1007/978-1-4757-1904-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/nav.3800020109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032778056"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12650-016-0374-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043472359", 
          "https://doi.org/10.1007/s12650-016-0374-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12650-016-0374-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043472359", 
          "https://doi.org/10.1007/s12650-016-0374-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02289565", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044215102", 
          "https://doi.org/10.1007/bf02289565"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02289565", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044215102", 
          "https://doi.org/10.1007/bf02289565"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/t-c.1969.222678", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061455087"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tii.2016.2628747", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061633010"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2010.2049235", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061642522"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnn.2010.2045657", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061717701"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvcg.2011.220", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061813610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tvcg.2013.150", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061814020"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/0111030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062837892"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/acv.1994.341300", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094190322"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12650-018-0476-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101057035", 
          "https://doi.org/10.1007/s12650-018-0476-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/access.2018.2872344", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107278218"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-04", 
    "datePublishedReg": "2019-04-01", 
    "description": "This paper proposes a novel unsupervised multi-dimensional scaling (MDS) method to visualize high-dimensional data and their relations in a low-dimensional (e.g., 2D) space. Different from traditional MDS approaches where the main purpose is to embed high-dimensional data into a low-dimensional space, this study aims to both embed data into a low-dimensional space and reveal data relations, thus providing better visualization as graph. By taking into account the density relationships inherent in data, this paper proposes a new density-concentrated multi-dimensional scaling algorithm DCMDS-RV to perform visualization of high-dimensional data and their relations. One benefit of the proposed DCMDS-RV algorithm is the ability to embed data more accurately than traditional MDS techniques by using second-order gradient optimization instead of first-order gradient. A key advantage of the presented DCMDS-RV algorithm is the capability to show relations as categorical information. In the resulting embedding, data are compact in clusters. The results demonstrate that the proposed DCMDS-RV algorithm outperforms conventional MDS methods regarding Kruskal stress factor and ACC value. The relations between data as graph are clearly viewed as well. ", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s12650-018-0532-0", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1033383", 
        "issn": [
          "1343-8875", 
          "1875-8975"
        ], 
        "name": "Journal of Visualization", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "22"
      }
    ], 
    "name": "DCMDS-RV: density-concentrated multi-dimensional scaling for relation visualization", 
    "pagination": "341-357", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "1e5d58d8804920d6e691fc20873964c9e48ab7732e4d0870adda1e8f2a282261"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12650-018-0532-0"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1110133955"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12650-018-0532-0", 
      "https://app.dimensions.ai/details/publication/pub.1110133955"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:24", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000369_0000000369/records_68969_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs12650-018-0532-0"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12650-018-0532-0'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12650-018-0532-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12650-018-0532-0'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12650-018-0532-0'


 

This table displays all metadata directly associated to this object as RDF triples.

153 TRIPLES      21 PREDICATES      48 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12650-018-0532-0 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N026e888e882544a68884a5ee9c492e1f
4 schema:citation sg:pub.10.1007/978-1-4757-1904-8
5 sg:pub.10.1007/bf02289565
6 sg:pub.10.1007/bf03181472
7 sg:pub.10.1007/s12650-011-0084-z
8 sg:pub.10.1007/s12650-014-0246-x
9 sg:pub.10.1007/s12650-016-0374-6
10 sg:pub.10.1007/s12650-018-0476-4
11 sg:pub.10.1186/1471-2105-8-3
12 https://doi.org/10.1002/nav.3800020109
13 https://doi.org/10.1073/pnas.1031596100
14 https://doi.org/10.1109/access.2018.2872344
15 https://doi.org/10.1109/acv.1994.341300
16 https://doi.org/10.1109/t-c.1969.222678
17 https://doi.org/10.1109/tii.2016.2628747
18 https://doi.org/10.1109/tip.2010.2049235
19 https://doi.org/10.1109/tnn.2010.2045657
20 https://doi.org/10.1109/tvcg.2011.220
21 https://doi.org/10.1109/tvcg.2013.150
22 https://doi.org/10.1126/science.1242072
23 https://doi.org/10.1126/science.290.5500.2319
24 https://doi.org/10.1137/0111030
25 schema:datePublished 2019-04
26 schema:datePublishedReg 2019-04-01
27 schema:description This paper proposes a novel unsupervised multi-dimensional scaling (MDS) method to visualize high-dimensional data and their relations in a low-dimensional (e.g., 2D) space. Different from traditional MDS approaches where the main purpose is to embed high-dimensional data into a low-dimensional space, this study aims to both embed data into a low-dimensional space and reveal data relations, thus providing better visualization as graph. By taking into account the density relationships inherent in data, this paper proposes a new density-concentrated multi-dimensional scaling algorithm DCMDS-RV to perform visualization of high-dimensional data and their relations. One benefit of the proposed DCMDS-RV algorithm is the ability to embed data more accurately than traditional MDS techniques by using second-order gradient optimization instead of first-order gradient. A key advantage of the presented DCMDS-RV algorithm is the capability to show relations as categorical information. In the resulting embedding, data are compact in clusters. The results demonstrate that the proposed DCMDS-RV algorithm outperforms conventional MDS methods regarding Kruskal stress factor and ACC value. The relations between data as graph are clearly viewed as well.
28 schema:genre research_article
29 schema:inLanguage en
30 schema:isAccessibleForFree false
31 schema:isPartOf N65c422192c1347e1bf04e0e6965c29b0
32 Ne72ce644162245d290b81b9c20803955
33 sg:journal.1033383
34 schema:name DCMDS-RV: density-concentrated multi-dimensional scaling for relation visualization
35 schema:pagination 341-357
36 schema:productId N1439555c4ed7455484e896a869af374b
37 N411baf4dd3ae4298b903624fc608eba6
38 N47fd2597b08749baa9fe26d99e6478d5
39 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110133955
40 https://doi.org/10.1007/s12650-018-0532-0
41 schema:sdDatePublished 2019-04-11T13:24
42 schema:sdLicense https://scigraph.springernature.com/explorer/license/
43 schema:sdPublisher Nb3390b1da0704b7c83ff51fa6448c7e3
44 schema:url https://link.springer.com/10.1007%2Fs12650-018-0532-0
45 sgo:license sg:explorer/license/
46 sgo:sdDataset articles
47 rdf:type schema:ScholarlyArticle
48 N026e888e882544a68884a5ee9c492e1f rdf:first sg:person.010606140353.42
49 rdf:rest N8e7fdc17ef4a48d19def08e1c9f96162
50 N1439555c4ed7455484e896a869af374b schema:name readcube_id
51 schema:value 1e5d58d8804920d6e691fc20873964c9e48ab7732e4d0870adda1e8f2a282261
52 rdf:type schema:PropertyValue
53 N411baf4dd3ae4298b903624fc608eba6 schema:name doi
54 schema:value 10.1007/s12650-018-0532-0
55 rdf:type schema:PropertyValue
56 N47fd2597b08749baa9fe26d99e6478d5 schema:name dimensions_id
57 schema:value pub.1110133955
58 rdf:type schema:PropertyValue
59 N65c422192c1347e1bf04e0e6965c29b0 schema:volumeNumber 22
60 rdf:type schema:PublicationVolume
61 N76b5c5c7a12d4fddbb45e9ba585ec84c rdf:first sg:person.01074224145.90
62 rdf:rest Ndaab09cf9b134332bd45fd7da69b2e99
63 N8e7fdc17ef4a48d19def08e1c9f96162 rdf:first sg:person.013171041706.95
64 rdf:rest N76b5c5c7a12d4fddbb45e9ba585ec84c
65 Nb3390b1da0704b7c83ff51fa6448c7e3 schema:name Springer Nature - SN SciGraph project
66 rdf:type schema:Organization
67 Ndaab09cf9b134332bd45fd7da69b2e99 rdf:first sg:person.010007435523.13
68 rdf:rest rdf:nil
69 Ne72ce644162245d290b81b9c20803955 schema:issueNumber 2
70 rdf:type schema:PublicationIssue
71 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
72 schema:name Information and Computing Sciences
73 rdf:type schema:DefinedTerm
74 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
75 schema:name Artificial Intelligence and Image Processing
76 rdf:type schema:DefinedTerm
77 sg:journal.1033383 schema:issn 1343-8875
78 1875-8975
79 schema:name Journal of Visualization
80 rdf:type schema:Periodical
81 sg:person.010007435523.13 schema:affiliation https://www.grid.ac/institutes/grid.252546.2
82 schema:familyName Nelms
83 schema:givenName R. M.
84 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010007435523.13
85 rdf:type schema:Person
86 sg:person.010606140353.42 schema:affiliation https://www.grid.ac/institutes/grid.252546.2
87 schema:familyName Wu
88 schema:givenName Bo
89 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010606140353.42
90 rdf:type schema:Person
91 sg:person.01074224145.90 schema:affiliation https://www.grid.ac/institutes/grid.252546.2
92 schema:familyName Wilamowski
93 schema:givenName Bogdan M.
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01074224145.90
95 rdf:type schema:Person
96 sg:person.013171041706.95 schema:affiliation https://www.grid.ac/institutes/grid.252546.2
97 schema:familyName Smith
98 schema:givenName James S.
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013171041706.95
100 rdf:type schema:Person
101 sg:pub.10.1007/978-1-4757-1904-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031639131
102 https://doi.org/10.1007/978-1-4757-1904-8
103 rdf:type schema:CreativeWork
104 sg:pub.10.1007/bf02289565 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044215102
105 https://doi.org/10.1007/bf02289565
106 rdf:type schema:CreativeWork
107 sg:pub.10.1007/bf03181472 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007626457
108 https://doi.org/10.1007/bf03181472
109 rdf:type schema:CreativeWork
110 sg:pub.10.1007/s12650-011-0084-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1006457820
111 https://doi.org/10.1007/s12650-011-0084-z
112 rdf:type schema:CreativeWork
113 sg:pub.10.1007/s12650-014-0246-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1026544049
114 https://doi.org/10.1007/s12650-014-0246-x
115 rdf:type schema:CreativeWork
116 sg:pub.10.1007/s12650-016-0374-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043472359
117 https://doi.org/10.1007/s12650-016-0374-6
118 rdf:type schema:CreativeWork
119 sg:pub.10.1007/s12650-018-0476-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101057035
120 https://doi.org/10.1007/s12650-018-0476-4
121 rdf:type schema:CreativeWork
122 sg:pub.10.1186/1471-2105-8-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009905742
123 https://doi.org/10.1186/1471-2105-8-3
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1002/nav.3800020109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032778056
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1073/pnas.1031596100 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014613620
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1109/access.2018.2872344 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107278218
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1109/acv.1994.341300 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094190322
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1109/t-c.1969.222678 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061455087
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1109/tii.2016.2628747 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061633010
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1109/tip.2010.2049235 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061642522
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1109/tnn.2010.2045657 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061717701
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1109/tvcg.2011.220 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061813610
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1109/tvcg.2013.150 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061814020
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1126/science.1242072 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027692695
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1126/science.290.5500.2319 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028334489
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1137/0111030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062837892
150 rdf:type schema:CreativeWork
151 https://www.grid.ac/institutes/grid.252546.2 schema:alternateName Auburn University
152 schema:name Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
153 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...