基于随机配置网络的轻量级人体行为识别模型

针对智能手机受限的计算和存储环境等问题,提出基于流形正则化和QR分解的轻量级随机配置网络人体行为识别模型.首先,利用流形正则化解决输入数据被随机映射到SCNs隐含层空间后出现难以预测的非线性分布问题,以提升模型结构的轻量性;其次,采用QR分解降低输出权值计算复杂度,进一步提高模型建模过程的轻量性;最后,在两个人体行为识别数据集上评估所提出模型在模型识别精度和轻量性方面的有效性.实验结果表明,与SCNs、CNN等相比,所提出模型对于人体行为识别问题不仅可以实现识别精度的提高,还能有效降低计算复杂度,提高模型结构的紧致性.

A lightweight model for human activity recognition using stochastic configuration networks

Aiming at the limited computing and storage environment of smart phones, this paper proposes a lightweight human activity recognition model based on stochastic configuration network with manifold regularization and QR decomposition. Firstly, manifold regularization is used to solve the problem of unpredictable nonlinear distribution after the input data are randomly mapped to the hidden layer space of SCNs, so as to improve the lightweight of the model structure. Secondly, QR decomposition is used to reduce the computational complexity of the output weights and further improve the lightweight of the model modeling process. Finally, based on two human activity data sets, the effectiveness of the proposed model in model recognition accuracy and lightweight is evaluated. Experimental results show that, compared with SCNs, CNN, etc, the proposed model can not only improve the accuracy of activity recognition, but also effectively reduce the calculation complexity and improve the compactness of the model structure.