Supervised Manifold Learning for NIR Modeling of Cigarette Brand Identification
For NIR data has the character of high dimension, nonlinear, and high noise, we often confront the problem of dimensionality reduction when building the classification model on Near-Infrared spectra data. Traditional classification methods and linear dimensionality reduction techniques are difficult to improve the model performance. In this paper, a novel nonlinear modeling for NIR spectra analysis was proposed by combining S-Isomap and KNN. S-Isomap is a supervised manifold learning method which can effectively find out the intrinsic low dimensional structure and extract important information from the raw data. Compared with KLLE, KPCA, and other classification methods such as SVM or LDA, the results show that S-Isomap-KNN method performs the best on the modeling of cigarette brand identification. The method is also a good technique for visualization.
Supervised manifold learning Near-Infrared spectra Dimensionality reduction S-ISOMAP KNN Cigarette band identification
Ying He Xiangqian Ding Lintao Ma
Information Engineering Center, Ocean University of China, 266071 Qingdao, China
国际会议
沈阳
英文
1258-1263
2011-11-22（万方平台首次上网日期，不代表论文的发表时间）