Continuous Semi-Supervised Nonnegative Matrix Factorization

Anand Somayajula; Deanna Needell; Feng Liu; Michael R. Lindstrom; Xiaofu Ding

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Continuous Semi-Supervised Nonnegative Matrix Factorization

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arxiv 2212.09858 v1 pith:KN4VM3L7 submitted 2022-12-19 cs.CL cs.LG

Continuous Semi-Supervised Nonnegative Matrix Factorization

Michael R. Lindstrom , Xiaofu Ding , Feng Liu , Anand Somayajula , Deanna Needell This is my paper

classification cs.CL cs.LG

keywords nonnegativefactorizationmatrixcontinuousregressiontopicsamountsapproximation

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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.

Continuous Semi-Supervised Nonnegative Matrix Factorization

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