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Papers/Semi-supervised NMF Models for Topic Modeling in Learning ...

Semi-supervised NMF Models for Topic Modeling in Learning Tasks

Jamie Haddock, Lara Kassab, Sixian Li, Alona Kryshchenko, Rachel Grotheer, Elena Sizikova, Chuntian Wang, Thomas Merkh, R. W. M. A. Madushani, Miju Ahn, Deanna Needell, Kathryn Leonard

2020-10-15Text ClassificationGeneral ClassificationClassification
PaperPDFCode(official)

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.

Results

TaskDatasetMetricValueModel
Text Classification20NEWSAccuracy81.88SSNMF
Classification20NEWSAccuracy81.88SSNMF

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