利用传感器多分辨图的时间散射进行机器嗅觉
Machine olfaction using time scattering of sensor multiresolution graphs
摘要：
在本文中，我们构建了一个学习结构的高维时间序列采样的传感器安排。使用冗余小波分解的传感器位置上构建的图，我们的算法是能够构建歧视性的功能，利用传感器之间的互信息。然后，该算法将散射网络应用于时间序列图以创建特征空间。我们证明了我们的方法上的机器嗅觉问题，其中一个需要分类的气体类型和位置，它起源于由传感器阵列采样的数据。我们的实验结果清楚地表明，我们的方法优于经典的机器学习技术在以前的研究中使用。
英文摘要：
In this paper we construct a learning architecture for high dimensional time series sampled by sensor arrangements. Using a redundant wavelet decomposition on a graph constructed over the sensor locations, our algorithm is able to construct discriminative features that exploit the mutual information between the sensors. The algorithm then applies scattering networks to the time series graphs to create the feature space. We demonstrate our method on a machine olfaction problem, where one needs to classify the gas type and the location where it originates from data sampled by an array of sensors. Our experimental results clearly demonstrate that our method outperforms classical machine learning techniques used in previous studies.
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