论文标题
多线性压缩学习中的性能指标
Performance Indicator in Multilinear Compressive Learning
论文作者
论文摘要
最近，提出了多线性压缩学习（MCL）框架，以有效地优化使用多维信号（即张量）时的感应和学习步骤。在通常的压缩学习中，尤其是在MCL中，由压缩传感设备捕获的压缩测量数量表征了存储要求或传输的带宽要求。但是，这个数字并未完全表征MCL系统的学习性能。在本文中，我们分析了输入信号分辨率，压缩测量的数量和MCL的学习性能之间的关系。我们的经验分析表明，在MCL初始化步骤中获得的重建误差与学习性能密切相关，因此可以充当一个很好的指标，可以有效地表征从不同传感器配置获得的学习性能而无需优化整个系统。
Recently, the Multilinear Compressive Learning (MCL) framework was proposed to efficiently optimize the sensing and learning steps when working with multidimensional signals, i.e. tensors. In Compressive Learning in general, and in MCL in particular, the number of compressed measurements captured by a compressive sensing device characterizes the storage requirement or the bandwidth requirement for transmission. This number, however, does not completely characterize the learning performance of a MCL system. In this paper, we analyze the relationship between the input signal resolution, the number of compressed measurements and the learning performance of MCL. Our empirical analysis shows that the reconstruction error obtained at the initialization step of MCL strongly correlates with the learning performance, thus can act as a good indicator to efficiently characterize learning performances obtained from different sensor configurations without optimizing the entire system.
