ScholarGate
Assistent
Machine learningDeep learning / NLP / CV

Forklarbar NMF-temamodell

En forklarbar NMF-temamodell kombinerer Non-negative Matrix Factorization — en del-basert dekomponering av en dokument-term-matrise — med eksplisitte tolkbarhetsteknikker som koherensmetrikker, ord-bidrags-scorer og SHAP-lignende attribusjon for å gjøre oppdagede temaer transparente og reviderbare for menneskelige lesere.

Åpne i MethodMindSnartVideoSnartDownload slides

Les hele metoden

Kun for medlemmer

Logg inn med en gratis konto for å lese denne delen.

Logg inn

Method map

The neighbourhood of related methods — select a node to explore.

Kilder

  1. Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562. link
  2. Non-negative matrix factorization. Wikipedia. link

Slik siterer du denne siden

ScholarGate. (2026, June 3). Explainable Non-negative Matrix Factorization Topic Model. ScholarGate. https://scholargate.app/no/deep-learning/explainable-nmf-topic-model

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

Compare side by side
ScholarGateExplainable NMF Topic Model (Explainable Non-negative Matrix Factorization Topic Model). Hentet 2026-06-15 fra https://scholargate.app/no/deep-learning/explainable-nmf-topic-model · Datasett: https://doi.org/10.5281/zenodo.20539026