Sensor data analysis is used in many application areas, for example, Artificial Intelligence of Things (AIoT), with the rapid developing of the deep neural network learning that promotes its application area. In this work, we propose the Depth and Width Changeable Deep Kernel Learning-based hyperspectral sensing data analysis algorithm. Compared with the traditional kernel learning-based hyperspectral data classification, the proposed method has its advantages on the hyperspectral data classification. With the deep kernel learning, the feature is mapped through many times mapping and has the more discriminative ability. So, the deep kernel learning has the better performance compared with the multiple kernels learning. And it has the ability to adjust the network architecture for hyperspectral data space, with the optimization equation of the span bound. The experiments are implemented to testified the feasibility and performance of the algorithms on the hyperspectral data analysis, with the classification accuracy of hyperspectral data. The comprehensive analysis of the experiments shows that the proposed algorithm is feasible to hyperspectral sensor data analysis and its promising classification method in many areas data analysis.

中文翻译：

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基于深度和宽度可变的基于网络的深核学习的高光谱传感器数据分析

传感器数据分析已用于许多应用领域，例如，物联网人工智能（AIoT），随着深度神经网络学习的迅速发展促进了其应用领域的发展。在这项工作中，我们提出了基于深度和宽度可变的深核学习的高光谱传感数据分析算法。与传统的基于核学习的高光谱数据分类相比，该方法在高光谱数据分类上具有优势。通过深入的内核学习，该功能可以通过多次映射进行映射，并具有更高的判别能力。因此，与多内核学习相比，深度内核学习具有更好的性能。而且它能够针对高光谱数据空间调整网络架构，与跨度边界的优化方程式。通过实验证明了该算法在高光谱数据分析中的可行性和性能，并具有高光谱数据的分类精度。实验的综合分析表明，该算法对高光谱传感器数据分析是可行的，并且在许多领域数据分析中具有广阔的应用前景。