Terahertz spectrum is sensitive to the change of the nonlocal molecular vibration mode. Accordingly, the spectral waveform is susceptible to variety of physical and chemical factors, which will lead to peak changes, frequency shifts, and even deformation of the overall waveform. Component analysis and material identification from the correspondence between the fixed peak features and materials will prone to cause errors or mistakes. Therefore, to solve this problem, we proposed a method based on Kernel Optimization Relevance Vector Machine (KO-RVM), which extracts global graphic features to distinct from the local features extraction method. And we use Support Vector Regression (SVR) algorithm as comparison. The result shows that, when basis functions’ parameters of RVM are optimized with expectation-maximization algorithm, it will be suitable for feature extraction of terahertz transmission spectrum. The spectrum can be sparsely represented, and the amount of extracted graphic features is substantially reduced. Reconstruction models based on these features are capable of retaining the overall spectral characteristics, and fitting results for each band are more consistent, while the extracted spectrum features can be used as basis of similarity measurement and the common characteristics investigation between different materials.

中文翻译：

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[基于内核优​​化相关向量机的太赫兹频谱特征提取]。

太赫兹光谱对非局部分子振动模式的变化敏感。因此，频谱波形易受各种物理和化学因素的影响，这将导致峰值变化，频率偏移甚至整个波形变形。从固定峰特征和材料之间的对应关系进行成分分析和材料识别将容易引起错误或错误。因此，为解决这一问题，我们提出了一种基于核优化相关向量机（KO-RVM）的方法，该方法提取全局图形特征以区别于局部特征提取方法。并且我们使用支持向量回归（SVR）算法作为比较。结果表明，当使用期望最大化算法优化RVM的基函数参数时，它适用于太赫兹传输频谱的特征提取。频谱可以稀疏地表示，并且提取的图形特征的数量大大减少。基于这些特征的重建模型能够保留整体光谱特征，并且每个频带的拟合结果更加一致，而提取的光谱特征可以用作相似性度量的基础以及不同材料之间的共同特征研究。