Metodebevisregistrering
NMF Topic Modeling
NMF topic modeling uses Non-negative Matrix Factorization — the parts-based decomposition introduced by Lee and Seung (1999) — to extract document-topic distributions from a corpus. By factoring a document-term matrix into two non-negative matrices, it recovers a small set of topics and tends to produce more interpretable topics than LDA.
Kilderegistrering
Citater kopieret ordret fra metodens kilderegistrering. Ingen påstandsniveauverifikation er udledt heraf.
Topic Modeling with Non-negative Matrix Factorization
Taksonomisk metoderegistrering · process-pipeline / text-mining
- Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791. · DOI 10.1038/44565
- Arora, S., Ge, R., Halpern, Y., Mimno, D., Moitra, A., Sontag, D., Wu, Y. & Zhu, M. (2013). A Practical Algorithm for Topic Modeling with Provable Guarantees. Proceedings of the 30th International Conference on Machine Learning (ICML), 280-288. · URL
Kuraterede påstande
Påstande gemt i bevis-loggen, hver med sin egen vurdering.
Ingen kuraterede påstande endnu
Denne visning opfinder ikke en påstandsvurdering, når loggen ingen har.
Relaterede metoder
Genereret fra metodegrafen og vist som maskinelt foreslåede relationer — ingen bevispåstand er udledt.