School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, 310018, China; School of Information Sciences, University of Illinois at Urbana Champaign, Champaign, IL, 61820-6211, USA.
School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing, 210023, China.
Talanta. 2020 May 1;211:120681. doi: 10.1016/j.talanta.2019.120681. Epub 2019 Dec 28.
Raman spectroscopy is widely used in discriminative tasks. It provides a wide-range physio-chemical fingerprint in a rapid and non-invasive way. The Raman spectrometry uses a sensor array to convert photon signals into digital spectroscopic data. This analog-to-digital process can benefit from the compressed sensing (CS) technique. The major benefits include less memory usage, shorter acquisition time, and more cost-efficient sensor. Traditional compressed sensing and reconstruction is a series of mathematical operations performed on the signal. Meanwhile, for discriminative tasks, both the signal and the categorical information are involved. For such scenarios, this paper proposes a method that uses both domain signal and categorical information to optimize CS hyper-parameters, including 1) the sampling ratio or the sensing matrix, 2) the basis matrix for the sparse transform, and 3) the regularization rate or shrinkage factor for L1-norm minimization. A case study of formula milk brand identification proves the proposed method can generate effective compressed sensing while preserving enough discriminative power in the reconstructed signal. Under the optimized hyper-parameters, a 100% classification accuracy is retained by only sampling 20% of the original signal.
拉曼光谱广泛应用于判别任务。它提供了一种快速、非侵入式的广泛的物理化学指纹。拉曼光谱仪使用传感器阵列将光子信号转换为数字光谱数据。这种模拟到数字的过程可以受益于压缩感知（CS）技术。主要优点包括更少的内存使用、更短的采集时间和更具成本效益的传感器。传统的压缩感知和重建是对信号执行的一系列数学操作。同时，对于判别任务，信号和类别信息都涉及到。针对这种情况，本文提出了一种使用域信号和类别信息来优化 CS 超参数的方法，包括 1）采样率或传感矩阵，2）稀疏变换的基矩阵，3）L1 范数最小化的正则化率或收缩因子。配方奶品牌识别的案例研究证明，所提出的方法可以在保持重构信号足够判别能力的同时生成有效的压缩感知。在优化的超参数下，仅对原始信号的 20%进行采样即可保留 100%的分类准确性。
