This paper explores the similarities of output layers in Neural Networks (NNs) with logistic regression to explain importance of inputs by Z-scores. The network analyzed, a network for fusion of Synthetic Aperture Radar (SAR) and Microwave Radiometry (MWR) data, is applied to prediction of arctic sea ice. With the analysis the importance of MWR relative to SAR is found to favor MWR components. Further, as the model represents image features at different scales, the relative importance of these are as well analyzed. The suggested methodology offers a simple and easy framework for analyzing output layer components and can reduce the number of components for further analysis with e.g. common NN visualization methods.
翻译:本文件探讨了神经网络中输出层与逻辑回归的相似性,以解释Z粒子投入的重要性。所分析的合成孔径雷达(SAR)和微波辐射测量(MWR)数据集成网络被应用于北极海冰的预测。通过分析发现最低孔径雷达相对于合成孔径雷达的重要性有利于最低孔径雷达的各个组成部分。此外,由于模型代表不同尺度的图像特征,因此对这些模型的相对重要性也进行了分析。建议的方法为分析输出层组成部分提供了一个简单易行的框架,并可以减少用于进一步分析的组件数量,例如通用的NNN可视化方法。