Continuous Semi-Supervised Nonnegative Matrix Factorization

• URL: http://arxiv.org/abs/2212.09858v1
• Date: Mon, 19 Dec 2022 21:07:27 GMT
• Title: Continuous Semi-Supervised Nonnegative Matrix Factorization
• Authors: Michael R. Lindstrom, Xiaofu Ding, Feng Liu, Anand Somayajula, Deanna Needell
• Abstract summary: Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. We show this factorization can be combined with regression on a continuous response variable.
• Score: 8.303018940526417
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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