论文标题
具有圆锥判别功能的低维易于解释的内核进行分类
Low-dimensional Interpretable Kernels with Conic Discriminant Functions for Classification
论文作者
论文摘要
内核通常被开发并用作隐式映射函数，由于其高维特征空间表示，它们显示出令人印象深刻的预测能力。在这项研究中，我们逐渐构建了一系列简单的特征地图，这些图导致一系列可解释的低维内核。在每个步骤中，我们都保留原始功能，并确保输入数据维度的增加非常低，以便由此产生的判别函数仍然可以解释并适合快速培训。尽管我们对可解释性的持续存在，但即使没有深入的高参数调整，我们也获得了高精度的结果。比较我们的结果与基准数据集上的几个知名核的比较表明，在预测准确性方面，提出的内核具有竞争力，而训练时间明显低于最先进的内核实现。
Kernels are often developed and used as implicit mapping functions that show impressive predictive power due to their high-dimensional feature space representations. In this study, we gradually construct a series of simple feature maps that lead to a collection of interpretable low-dimensional kernels. At each step, we keep the original features and make sure that the increase in the dimension of input data is extremely low, so that the resulting discriminant functions remain interpretable and amenable to fast training. Despite our persistence on interpretability, we obtain high accuracy results even without in-depth hyperparameter tuning. Comparison of our results against several well-known kernels on benchmark datasets show that the proposed kernels are competitive in terms of prediction accuracy, while the training times are significantly lower than those obtained with state-of-the-art kernel implementations.
