核优化相关向量机太赫兹频谱特征提取方法

太赫兹频谱对分子非局域振动模式的变化较为敏感。因此，其波形容易受到多种理化因素的影响，会产生峰值改变、频移，甚至整体波形的变化，单一地从固定峰值特征与物质的对应关系上进行组分分析和物质鉴别容易产生较大误差甚至错误。针对此问题，提出区别于局部特征提取方法的基于核优化相关向量机(KO-RVM)的整体图形特征提取方法，并与支持向量回归算法(SVR)进行比较。结果表明，经过期望最大化算法进行基函数参数控制的RVM适用于太赫兹透射谱的特征提取，可对每种物质的光谱数据进行稀疏表示，控制提取图形特征的数量。利用已提取特征构造的模型能够还原频谱曲线的整体特征，对谱线各频段的拟合效果更加一致，同时所提取的特征还可作为不同物质间太赫兹光谱相似性度量和共同特征发现的依据。

Terahertz Spectrum Features Extraction Based on Kernel Optimization Relevance Vector Machine

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 spec-tral 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.