论文标题
顺序数据的卷积签名
Convolutional Signature for Sequential Data
论文作者
论文摘要
签名是已知表征几何粗糙路径的无限分级序列，其中包括具有有界变化的路径。该对象已成功地研究了机器学习，主要是在低维情况下进行应用。在高维情况下，它遭受截断的特征变换中特征数量的指数增长。我们提出了一个基于神经网络的新型模型，该模型从卷积神经网络中借用了这个问题来解决这个问题。我们的模型以数据依赖性方式有效地减少了功能的数量。提供了一些经验实验来支持我们的模型。
Signature is an infinite graded sequence of statistics known to characterize geometric rough paths, which includes the paths with bounded variation. This object has been studied successfully for machine learning with mostly applications in low dimensional cases. In the high dimensional case, it suffers from exponential growth in the number of features in truncated signature transform. We propose a novel neural network based model which borrows the idea from Convolutional Neural Network to address this problem. Our model reduces the number of features efficiently in a data dependent way. Some empirical experiments are provided to support our model.
