School of Engineering, Nanyang Polytechnic, Singapore 569830, Singapore.
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.
Sensors (Basel). 2021 Jan 16;21(2):603. doi: 10.3390/s21020603.
A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data. In this work, the temporal context of the time series data is chosen as the useful aspect of the data that is passed through the network for learning. By exploiting the compositional locality of the time series data at each level of the network, shift-invariant features can be extracted layer by layer at different time scales. The temporal context is made available to the deeper layers of the network by a set of data processing operations based on the concatenation operation. A matching learning algorithm for the revised network is described in this paper. It uses gradient routing in the backpropagation path. The framework as proposed in this work attains better generalization without overfitting the network to the data, as the weights can be pretrained appropriately. It can be used end-to-end with multivariate time series data in their raw form, without the need for manual feature crafting or data transformation. Data experiments with electroencephalogram signals and human activity signals show that with the right amount of concatenation in the deeper layers of the proposed network, it can improve the performance in signal classification.
与复杂数据函数匹配的神经网络很可能会提高分类性能，因为它能够学习高度变化数据的有用方面。在这项工作中，选择时间序列数据的时间上下文作为通过网络进行学习的数据的有用方面。通过利用网络各层中时间序列数据的组合局部性，可以在不同的时间尺度上逐层提取平移不变特征。通过基于连接操作的一组数据处理操作，将时间上下文提供给网络的更深层。本文描述了用于修改后的网络的匹配学习算法。它在反向传播路径中使用梯度路由。所提出的框架在不过度拟合数据的情况下实现了更好的泛化，因为可以适当地对权重进行预训练。它可以与原始形式的多元时间序列数据端到端使用，而无需手动特征制作或数据转换。使用脑电图信号和人体活动信号进行的数据实验表明，在提出的网络的更深层中进行适当数量的连接，可以提高信号分类的性能。
