In this paper, a novel telescopic broad Bayesian learning (TBBL) is proposed for sequential learning. Conventional broad learning suffers from the singularity problem induced by the complexity explosion as data are accumulated. The proposed TBBL successfully overcomes the challenging issue and is feasible for sequential learning with big data streams. The learning network of TBBL is reconfigurable to adopt network augmentation and condensation. As time evolves, the learning network is augmented to incorporate the newly available data and additional network components. Meanwhile, the learning network is condensed to eliminate the network connections and components with insignificant contributions. Moreover, as a benefit of Bayesian inference, the uncertainty of the estimates can be quantified. To demonstrate the efficacy of the proposed TBBL, the performance on highly nonstationary piecewise time series and complex multivariate time series with 100 million data points are presented. Furthermore, an application for long-term structural health monitoring is presented.

中文翻译：

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大数据流的伸缩广泛贝叶斯学习


在本文中，提出了一种新颖的伸缩广泛贝叶斯学习（TBBL）用于顺序学习。传统的广泛学习面临着随着数据积累而复杂性爆炸引起的奇点问题。所提出的 TBBL 成功克服了具有挑战性的问题，并且对于大数据流的顺序学习是可行的。 TBBL的学习网络可重构以采用网络增强和压缩。随着时间的发展，学习网络得到增强，以合并新的可用数据和其他网络组件。同时，对学习网络进行压缩，消除贡献不显着的网络连接和组件。此外，作为贝叶斯推理的一个好处，可以量化估计的不确定性。为了证明所提出的 TBBL 的有效性，我们展示了在高度非平稳分段时间序列和具有 1 亿个数据点的复杂多元时间序列上的性能。此外，还提出了长期结构健康监测的应用。