论文标题
部分可观测时空混沌系统的无模型预测
End-to-End Signal Classification in Signed Cumulative Distribution Transform Space
论文作者
论文摘要
本文使用签名的累积分布变换（SCDT）提出了一种新的端到端信号分类方法。我们采用基于运输的生成模型来定义分类问题。然后，我们利用SCDT的数学属性来使问题更容易在变换域中，并使用SCDT域中最近的本地子空间（NLS）搜索算法来解决未知样本的类别。实验表明，所提出的方法提供了高准确性的分类结果，同时又有数据效率，对分布样本的稳健性以及相对于深度学习端到端分类方法的计算复杂性而具有竞争力。在Python语言中的实现将其作为软件包Pytranskit（https://github.com/rohdelab/pytranskit）的一部分集成。
This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT). We adopt a transport-based generative model to define the classification problem. We then make use of mathematical properties of the SCDT to render the problem easier in transform domain, and solve for the class of an unknown sample using a nearest local subspace (NLS) search algorithm in SCDT domain. Experiments show that the proposed method provides high accuracy classification results while being data efficient, robust to out-of-distribution samples, and competitive in terms of computational complexity with respect to the deep learning end-to-end classification methods. The implementation of the proposed method in Python language is integrated as a part of the software package PyTransKit (https://github.com/rohdelab/PyTransKit).
