关键词: 激光诱导荧光光谱, 特征提取, PCA, FLDA, 支持向量机

Abstract: Combined with Principal Component Analysis （PCA） and Fisher Linear Discriminative Analysis （FLDA） to extract features，a novel fluorescence spectra feature extraction algorithm named PCA_FLDA is presented in this paper.The experiment results show that this method can improve the diagnostic rate of earlier stage colonic cancer with laser-induced fluorescence spectra.After preprocessing the collected 240 spectra，50 feature variants were extracted with PCA_FLDA.By means of the support vector machine，all spectra are classified into two categories as the normal or the abnormal one.The sensitivity，specificity and discriminating accuracy are reached to 97.5%，95.12% and 96.25%，respectively.

Key words: laser-induced fluorescence spectra, feature extraction, Principal Component Analysis（PCA）, Fisher Linear Discriminative Analysis（FLDA）, Support Vector Machine（SVM）