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A canonical polyadic deep convolutional computation model for big data feature learning in Internet of Things

机译：物联网中大数据特征学习的典范多深度深度卷积计算模型

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摘要

In recent years, the Internet of Things is more widely deployed with increasing amounts of data gathered. These data are of high volume, velocity, veracity and variety, posing a vast challenge on the data analysis, especially with respect to variety and velocity. To address this challenge, a canonical polyadic deep convolutional computation model is introduced to efficiently and effectively capture the hierarchical representation of the big data by employing the canonical polyadic decomposition to factorize the deep convolutional computation. In particular, to speed up the learning of local topologies hidden in the big data, a canonical polyadic convolutional kernel is devised by compacting the tensor convolutional kernel into the linear combination of the principle rank-1 tensors. Furthermore, the canonical polyadic tensor fully-connected weight is used to efficiently map the correlation in the fully-connected layer. After that, the canonical polyadic high-order back-propagation is devised to train the canonical polyadic deep convolutional computation model. At last, detailed experiments are carried out on two well-known datasets. And results illustrate that the introduced model achieves higher performance than a competing model. (C) 2019 Elsevier B.V. All rights reserved.
机译：近年来，随着收集的数据量越来越大，物联网得到了更广泛的部署。这些数据具有高容量，高速度，准确性和多样性，这对数据分析提出了巨大挑战，尤其是在多样性和速度方面。为了解决这一挑战，引入了典范的多元深度卷积计算模型，以通过采用典范的多元分解分解深度卷积计算来有效地捕获大数据的层次表示。特别是，为了加快对隐藏在大数据中的局部拓扑的学习，通过将张量卷积核压缩为主秩1张量的线性组合来设计规范的多元卷积核。此外，规范多态张量全连接权重用于有效地映射全连接层中的相关性。在此之后，设计了规范的多元双峰高阶反向传播，以训练规范的多元双峰深度卷积计算模型。最后，对两个著名的数据集进行了详细的实验。结果表明，引入的模型比竞争模型具有更高的性能。 （C）2019 Elsevier B.V.保留所有权利。

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