For the Visual Internet of Things (VIoT), heterogeneous sensors such as visual cameras and acoustic microphones can be installed in devices to perform cognitive sensing and collect multiview data. Nonetheless, not every device has the same set of sensors due to deployment costs or sensor malfunctioning. This subsequently causes incomplete heterogeneous data, which means that data from various sensors may not be intact. For example, visual data are present, whereas audio signals are unavailable. Such phenomena may become severe when a large scale of VIoT devices are involved during crowdsensing, not to mention that missing values might occur in the collected data. In view of such, this study proposes solutions based on half quadratic (HQ) optimization to conquer the above problems in the VIoT. In this article, challenges involving incomplete heterogeneous multiview (IHM) data are investigated: IHM data reconstruction, IHM feature extraction, and IHM data recognition. Moreover, the corresponding solutions to the above challenges - HQ matrix completion, HQ supervised discrete hashing, and HQ graph neural networks - are also introduced in this article. Experiments on various HQ functions were carried out to examine their effectiveness, and the experimental results verify the proposed solutions.

中文翻译：

---

基于半二次优化的视觉物联网异构多视角人群感知

对于视觉物联网 (VIoT)，可以在设备中安装视觉摄像头和声学麦克风等异构传感器，以执行认知感知并收集多视图数据。尽管如此，由于部署成本或传感器故障，并非每个设备都具有相同的传感器组。这随后会导致异构数据不完整，这意味着来自各种传感器的数据可能不完整。例如，存在视觉数据，而音频信号不可用。当人群感知过程中涉及大规模的 VIoT 设备时，这种现象可能会变得严重，更不用说收集的数据中可能会出现缺失值。鉴于此，本研究提出了基于半二次（HQ）优化的解决方案，以克服 VIoT 中的上述问题。在本文中，研究了涉及不完整异构多视图 (IHM) 数据的挑战：IHM 数据重建、IHM 特征提取和 IHM 数据识别。此外，本文还介绍了上述挑战的相应解决方案——HQ 矩阵补全、HQ 监督离散散列和 HQ 图神经网络。对各种 HQ 功能进行了实验以检验其有效性，实验结果验证了所提出的解决方案。