2022/12/19 by Michael R. Lindstrom, Lindstrom, Michael R., Xiaofu Ding +7
Computer Science · #Text and Document Classification Technologies #Face and Expression Recognition #Educational Technology and Assessment
paper · pdf · doi:10.48550/arxiv.2212.09858
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.