In this work, we investigated the application of score-based gradient learning in discriminative and generative classification settings. Score function can be used to characterize data distribution as an alternative to density. It can be efficiently learned via score matching, and used to flexibly generate credible samples to enhance discriminative classification quality, to recover density and to build generative classifiers. We analysed the decision theories involving score-based representations, and performed experiments on simulated and real-world datasets, demonstrating its effectiveness in achieving and improving binary classification performance, and robustness to perturbations, particularly in high dimensions and imbalanced situations.
翻译:在这项工作中,我们调查了在歧视性和基因化分类环境中应用基于分数的梯度学习的情况。计分功能可以用来描述数据分布,作为密度的替代物。可以通过比分匹配有效地学习,并用来灵活生成可靠的样本,以提高歧视性分类质量,恢复密度,并建设基于分数的分类方法。我们分析了涉及基于分数的表示方式的决策理论,并对模拟和现实世界数据集进行了实验,表明其在实现和改进二进制分类性能方面的有效性,以及对扰动的稳健性,特别是在高度和不平衡的情况下。