A novel OCC method for human action recognition namely the Laplacian One Class Extreme Learning Machines is presented. The proposed method exploits local geometric data information within the OC-ELM optimization process. It is shown that emphasizing on preserving the local geometry of the data leads to a regularized solution, which models the target class more efficiently than the standard OC-ELM algorithm. The proposed method is extended to operate in feature spaces determined by the network hidden layer outputs, as well as in ELM spaces of arbitrary dimensions. Its superior performance against other OCC options is consistent among five publicly available human action recognition datasets.
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机译：提出了一种新颖的用于人类动作识别的OCC方法，即Laplacian One Class Extreme Learning Machines。所提出的方法在OC-ELM优化过程中利用了局部几何数据信息。结果表明，强调保留数据的局部几何形状会导致一种正规化的解决方案，该解决方案比标准OC-ELM算法更有效地对目标类别进行建模。所提出的方法被扩展以在由网络隐藏层输出确定的特征空间以及任意尺寸的ELM空间中操作。在五个公开的人类动作识别数据集中，其相对于其他OCC选项的优越性能是一致的。
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