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Continuous Semi-Supervised Nonnegative Matrix Factorization

arXiv 2212.09858 ↗First posted 19 Dec 2022cs.CL cs.LGcited by 0

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Abstract (arXiv)

Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In this paper, we show this factorization can be combined with regression on a continuous response variable. In practice, the method performs better than regression done after topics are identified and retrains interpretability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 28 references that are in the map. First posted on arXiv 19 Dec 2022.

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4 of 28 references.

References · 4

  1. 2020A critical review of LASSO and its derivatives for variable selection under dependence among covariatescited by 8
  2. 2020Semi-supervised NMF Models for Topic Modeling in Learning Taskscited by 6
  3. 2022Guided Semi-Supervised Non-negative Matrix Factorization on Legal Documentscited by 5
  4. 2022Semi-supervised Nonnegative Matrix Factorization for Document Classificationcited by 2

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