ABSTRACT Due to reduction in dimensionality and extraction of the definitive features of input data, deep architectures have achieved significant success in various machine learning applications. Considering their successful applications in speech recognition and image classification, the main goal of this research is to investigate the performance of the sparse autoencoders utilized in regression analysis. To this end, deep sparse autoencoders with the standard method of training, cascaded, and partially cascaded architectures, fed with the fusion of low- and high-level features, are proposed and implemented. The regression task is to forecast the vehicular flow rate of a location on an arterial highway using different traffic variables of several locations ahead in the Twin Cities Metro area of Minneapolis. The results demonstrate that the partially cascaded model exhibits advancements in yielding more accurate results than the other two architectures fed with the features that correlate the most to the traffic flow rate.

中文翻译：

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深度自动编码器的特征融合模型：在交通流预测中的应用

摘要 由于降维和提取输入数据的明确特征，深度架构在各种机器学习应用中取得了重大成功。考虑到它们在语音识别和图像分类中的成功应用，本研究的主要目标是研究用于回归分析的稀疏自动编码器的性能。为此，提出并实施了具有标准训练方法、级联和部分级联架构的深度稀疏自动编码器，并融合了低级和高级功能。回归任务是使用明尼阿波利斯双城都会区前方多个位置的不同交通变量来预测主干公路上某个位置的车辆流量。