论文标题
用于学习任务中主题建模的半监督NMF模型
Semi-supervised NMF Models for Topic Modeling in Learning Tasks
论文作者
论文摘要
我们提出了几个新的模型，用于半监督的非负矩阵分解（SSNMF），并为SSNMF模型提供动机，因为鉴于特定的不确定性分布，我们作为最大似然估计量。我们为每个新模型提供了乘法更新方法，并演示了这些模型在分类中的应用，尽管它们对其他有监督的学习任务具有灵活性。我们说明了这些模型和培训方法对综合数据和真实数据的希望，并在20个新闻组数据集上实现了高分类精度。
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.
