Abstract：In this paper, the opinion is presented that category feature includes two kinds: classification feature and recognition feature. The idea is described that category feature can be received by traditional PCA (principal component analysis) and improved PCA. The example calculating category feature is given in this paper. The analysis shows that the recognition precision will be improved greatly as the unknown target is compared twice by two kinds of category feature.
李军梅，胡以华. 目标类特征确定中的主成分分析法研究及在目标识别中的应用[J]. 模式识别与人工智能, 2006, 19(1): 106-110.
LI JunMei, HU YiHua. Research on Principal Component Analysis in Choosing Target Category Feature and Its Application to Target Recognition. , 2006, 19(1): 106-110.
[1] Martinez A M, Kak A C. PCA Versus LDA. IEEE Trans on Pattern Analysis and Machine Intelligence, 2001, 23(2): 228-233 [2] Zhao W Y, Krishnaswamy A, Chellappa R, et al. Discriminant Analysis of Principal Component for Face Recognition. In: Proc of the 3rd International Conference on Automatic Face and Gesture Recognition. Nara, Japan, 1998, 336-341 [3] de s Marquess J P. Pattern Recognition-Concepts, Methods and Application. Heidelberg, Germany: Springer-Verlag, 2001 (de s Marquess J P,著;吴逸飞,译. 模式识别——原理、方法及应用. 北京: 清华大学出版社, 2003) [4] Fan J C, Mei C L. Data Analysis. Beijing, China: Science Press, 2002 (in Chinese) (范金城,梅长林. 数据分析. 北京: 科学出版社, 2002) [5] Li Y, Lü K H. The Application of the Principal Components Analysis(PCA) to Debris Recognition. Journal of National University of Defense Technology, 2004, 26(1): 89-94 (in Chinese) (李 岳,吕克洪.主成分分析在铁谱磨粒识别中的应用研究. 国防科技大学学报, 2004, 26(1): 89-94) [6] Yang H L, Can Y, Chen G J, Wu Y X. Principal Component Analysis Based Artificial Neural Networks for Arc Welding Quality Control. Transactions of the China Welding Institution, 2003, 24(4): 55-58 (in Chinese) (杨海澜,蔡 艳,陈庚军,吴毅雄.主成分分析结合神经网络技术在焊接质量控制中的应用. 焊接学报, 2003, 24(4): 55-58) [7] Wang X R, Wang S G. Static Analysis of Practical Multivariate. Shanghai, China: Shanghai Science and Technology Publishers, 1990 (in Chinese) (王学仁,王松桂.实用多元统计分析. 上海: 上海科学技术出版社, 1990)
2006, Vol. 19 
