Correspondence factor analysis: An outline of its method View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1975-02

AUTHORS

H. Teil

ABSTRACT

Correspondence factor analysis is a multivariate technique that may be applied to any type of data and to any number of data points. It detects associations and oppositions existing between subjects and objects, measuring their contribution to the total inertia for each factor. The probabilistic character of the data matrix is taken into consideration, and together with the principle of distributional equivalence, results in stability. The projection of the subjects and the objects onto the same set of factorial axes enables two-dimensional graphs to be drawn which offer aid in the interpretation of the results. More... »

PAGES

3-12

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf02080630

DOI

http://dx.doi.org/10.1007/bf02080630

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003812284


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/2008/0101", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Pure Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "Centre d'Informatique G\u00e9ologique, Ecole Nationale Sup\u00e9rieure des Mines de Paris Fontainebleau, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Teil", 
        "givenName": "H.", 
        "id": "sg:person.013576657337.59", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013576657337.59"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "1975-02", 
    "datePublishedReg": "1975-02-01", 
    "description": "Correspondence factor analysis is a multivariate technique that may be applied to any type of data and to any number of data points. It detects associations and oppositions existing between subjects and objects, measuring their contribution to the total inertia for each factor. The probabilistic character of the data matrix is taken into consideration, and together with the principle of distributional equivalence, results in stability. The projection of the subjects and the objects onto the same set of factorial axes enables two-dimensional graphs to be drawn which offer aid in the interpretation of the results.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf02080630", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1039818", 
        "issn": [
          "1874-8961", 
          "1874-8953"
        ], 
        "name": "Mathematical Geosciences", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "7"
      }
    ], 
    "name": "Correspondence factor analysis: An outline of its method", 
    "pagination": "3-12", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "12297874c02e49b20f05274dc0160b9db78eaca1010f7dce7fa484fd8d1eed19"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf02080630"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1003812284"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf02080630", 
      "https://app.dimensions.ai/details/publication/pub.1003812284"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T14:57", 
    "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/v90/0000000001_0000000264/records_8663_00000498.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF02080630"
  }
]
 

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/bf02080630'

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/bf02080630'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf02080630'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf02080630'


 

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

60 TRIPLES      20 PREDICATES      27 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf02080630 schema:about anzsrc-for:01
2 anzsrc-for:0101
3 schema:author Nbb126563ac934fa295b821d790d50bdf
4 schema:datePublished 1975-02
5 schema:datePublishedReg 1975-02-01
6 schema:description Correspondence factor analysis is a multivariate technique that may be applied to any type of data and to any number of data points. It detects associations and oppositions existing between subjects and objects, measuring their contribution to the total inertia for each factor. The probabilistic character of the data matrix is taken into consideration, and together with the principle of distributional equivalence, results in stability. The projection of the subjects and the objects onto the same set of factorial axes enables two-dimensional graphs to be drawn which offer aid in the interpretation of the results.
7 schema:genre research_article
8 schema:inLanguage en
9 schema:isAccessibleForFree false
10 schema:isPartOf N97c84e9d55a84486bece46da93282e8d
11 Nc8f9decc1aa44b0785d0fac5047d131c
12 sg:journal.1039818
13 schema:name Correspondence factor analysis: An outline of its method
14 schema:pagination 3-12
15 schema:productId Nc93d6521a386465a9a340c22c6f6e9c7
16 Nf63edb6dfdb1496494c21b66283bdf59
17 Nfb3127f9459e404da0b6377005cd914a
18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003812284
19 https://doi.org/10.1007/bf02080630
20 schema:sdDatePublished 2019-04-10T14:57
21 schema:sdLicense https://scigraph.springernature.com/explorer/license/
22 schema:sdPublisher N0b10a460a35a4907b23896e4d96f5b10
23 schema:url http://link.springer.com/10.1007/BF02080630
24 sgo:license sg:explorer/license/
25 sgo:sdDataset articles
26 rdf:type schema:ScholarlyArticle
27 N0b10a460a35a4907b23896e4d96f5b10 schema:name Springer Nature - SN SciGraph project
28 rdf:type schema:Organization
29 N27d059cb93c8469dbd33b04223459a1b schema:name Centre d'Informatique Géologique, Ecole Nationale Supérieure des Mines de Paris Fontainebleau, France
30 rdf:type schema:Organization
31 N97c84e9d55a84486bece46da93282e8d schema:issueNumber 1
32 rdf:type schema:PublicationIssue
33 Nbb126563ac934fa295b821d790d50bdf rdf:first sg:person.013576657337.59
34 rdf:rest rdf:nil
35 Nc8f9decc1aa44b0785d0fac5047d131c schema:volumeNumber 7
36 rdf:type schema:PublicationVolume
37 Nc93d6521a386465a9a340c22c6f6e9c7 schema:name doi
38 schema:value 10.1007/bf02080630
39 rdf:type schema:PropertyValue
40 Nf63edb6dfdb1496494c21b66283bdf59 schema:name readcube_id
41 schema:value 12297874c02e49b20f05274dc0160b9db78eaca1010f7dce7fa484fd8d1eed19
42 rdf:type schema:PropertyValue
43 Nfb3127f9459e404da0b6377005cd914a schema:name dimensions_id
44 schema:value pub.1003812284
45 rdf:type schema:PropertyValue
46 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
47 schema:name Mathematical Sciences
48 rdf:type schema:DefinedTerm
49 anzsrc-for:0101 schema:inDefinedTermSet anzsrc-for:
50 schema:name Pure Mathematics
51 rdf:type schema:DefinedTerm
52 sg:journal.1039818 schema:issn 1874-8953
53 1874-8961
54 schema:name Mathematical Geosciences
55 rdf:type schema:Periodical
56 sg:person.013576657337.59 schema:affiliation N27d059cb93c8469dbd33b04223459a1b
57 schema:familyName Teil
58 schema:givenName H.
59 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013576657337.59
60 rdf:type schema:Person
 




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


...