Semi-supervised NMF Models for Topic Modeling in Learning Tasks
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Publication:6351426
arXiv2010.07956MaRDI QIDQ6351426
Author name not available (Why is that?)
Publication date: 15 October 2020
Abstract: We propose several new models for semi-supervised nonnegative matrix factorization (SSNMF) and provide motivation for SSNMF models as maximum likelihood estimators given specific distributions of uncertainty. We present multiplicative updates training methods for each new model, and demonstrate the application of these models to classification, although they are flexible to other supervised learning tasks. We illustrate the promise of these models and training methods on both synthetic and real data, and achieve high classification accuracy on the 20 Newsgroups dataset.
Has companion code repository: https://github.com/jamiehadd/ssnmf
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