太赫兹时域光谱技术是一门新兴光谱检测技术,广泛应用于安检及反恐、生物医学和食品质量检测等方面.太赫兹谱的分类识别技术是太赫兹光谱检测技术的一个重要环节.由于受到噪声的影响,太赫兹谱可能在高维空间中成复杂的非线性分布,传统的分类方法难以取得理想的分类效果.流形学习和支持向量机都是当前机器学习领域的研究热点,都采取了核方法来解决非线性问题,正因为两者之间有很多共通之处,将这两种方法充分结合提出了一种称之为ISOMAP-SVM的新算法.这种新算法拥有比传统的支持向量机算法更快的训练速度和更好的分类效果.实验结果表明利用新算法可以实现对不同种类药品的识别,为太赫兹光谱技术用于药品的检测和识别提供了一种新的有效方法.%Terahertz time-domain spectroscopy is a new spectrum detection technology, and applied to various research fields:national security and defense, biomedical and food quality inspection. The classification of THz spectrums is one crucial domain of THz measurement. Because of the influence of noise, the result of traditional pattern classification methods is not satisfactory. Manifold learning and SVM are both effective approaches for non-linear pattern analysis problems and are also new research focus in current machine learning community. A new algorithm based on manifold learning and SVM is proposed in this paper. Compared to traditional SVM, this algorithm can improve the classification performance of SVM. Experimental result demonstrates that this algorithm can be applied to medical identification. It provides an effec-tive method for the detection and identification of medical by terahertz spectroscopy technology.
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