论文标题
半监督的非负矩阵分解用于文档分类
Semi-supervised Nonnegative Matrix Factorization for Document Classification
论文作者
论文摘要
我们提出了新的半监督非负矩阵分解（SSNMF）模型，用于文档分类，并为这些模型作为最大似然估计器提供动力。拟议的SSNMF模型同时提供了主题模型和分类模型，从而提供了高度可解释的分类结果。我们使用每个新模型的乘法更新来得出培训方法，并演示这些模型在单标签和多标签文档分类中的应用，尽管这些模型可以灵活地适合其他监督的学习任务，例如回归。我们说明了这些模型和培训方法对文档分类数据集的承诺（例如20个新闻组，路透社）。
We propose new semi-supervised nonnegative matrix factorization (SSNMF) models for document classification and provide motivation for these models as maximum likelihood estimators. The proposed SSNMF models simultaneously provide both a topic model and a model for classification, thereby offering highly interpretable classification results. We derive training methods using multiplicative updates for each new model, and demonstrate the application of these models to single-label and multi-label document classification, although the models are flexible to other supervised learning tasks such as regression. We illustrate the promise of these models and training methods on document classification datasets (e.g., 20 Newsgroups, Reuters).
