A manifold learning approach for gesture recognition from micro-Doppler radar measurements
A manifold learning approach for gesture recognition from micro-Doppler radar measurements
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DOI：
10.1016/j.neunet.2022.04.024
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发表时间：
2022-05-19
期刊：
影响因子：
7.8
通讯作者：
Guo，Adam
中科院分区：
文献类型：
--
作者：
Mason，E. S.;Mhaskar，H. N.;Guo，Adam
最近的一篇论文（Mhaskar（2020））介绍了一种简单明了的基于核的流形学习近似，除了维数之外，它不需要关于流形的任何知识。在本文中，我们研究了如何使用最小二乘优化近似的逐点误差的基础上类似本地化的内核取决于数据的特性和恶化，因为一个远离训练数据。该理论提出了一个抽象的本地化内核，它可以利用任何先验知识的数据位于一个未知的子流形的已知manifold.We展示了我们的方法使用公开的微多普勒数据集的性能，并调查使用不同的预处理措施，内核，和流形尺寸。具体而言，研究表明，上述论文中引入的局部化内核与PCA组件一起使用时，可以获得接近深度神经网络的竞争性能，并在训练速度和内存要求方面提供显着改进。为了证明我们的方法对领域知识是不可知的，我们在一个简单的视频数据集中研究了分类问题。
A recent paper (Mhaskar (2020)) introduces a straightforward and simple kernel based approximation for manifold learning that does not require the knowledge of anything about the manifold, except for its dimension. In this paper, we examine how the pointwise error in approximation using least squares optimization based on similarly localized kernels depends upon the data characteristics and deteriorates as one goes away from the training data. The theory is presented with an abstract localized kernel, which can utilize any prior knowledge about the data being located on an unknown sub-manifold of a known manifold.We demonstrate the performance of our approach using a publicly available micro-Doppler data set, and investigate the use of different preprocessing measures, kernels, and manifold dimensions. Specifically, it is shown that the localized kernel introduced in the above mentioned paper when used with PCA components leads to a near-competitive performance to deep neural networks, and offers significant improvements in training speed and memory requirements. To demonstrate the fact that our methods are agnostic to the domain knowledge, we examine the classification problem in a simple video data set.