Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization
Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corpora as two-dimensional scatter plots, reflecting s...
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Détails bibliographiques
| Publié dans: | IEEE transactions on visualization and computer graphics. - 1996. - 30(2024), 1 vom: 23. Jan., Seite 902-912
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| Auteur principal: |
Atzberger, Daniel
(Auteur) |
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| Autres auteurs: |
Cech, Tim,
Trapp, Matthias,
Richter, Rico,
Scheibel, Willy,
Dollner, Jurgen,
Schreck, Tobias |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2024
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| Accès à la collection: | IEEE transactions on visualization and computer graphics
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| Sujets: | Journal Article |
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