Abstract：Based on the optimization of the initial basis of the feature space, the Triangle Matrix Feature Transformation method is proposed to calculate the transformation matrix 公式 in feature extraction. In this method, the number of the unknown parameters in the transformation matrix is only half of that in the currently used methods. Thus it reduces the calculation pressure a lot. This method supports various feature transformation criterions, and it is of good flexibility.
[1] Wang Xuechuan , Paliwal K K. Feature Extraction and Dimensionality Reduction Algorithms and Their Applications in Vowel Recognition. Pattern Recognition, 2003, 36(10): 2429-2439 [2] Hyvarinen A, Oja E. Independent Component Analysis: Algorithms and Applications. Neural Networks, 2000, 13(4): 411-430 [3] Sun Darui, Wu Lenan. Orthonormal Component Discriminant Analysis for Face Recognition. Pattern Recognition and Artificial Intelligence, 2001, 14(3): 372-375 (in Chinese) (孙大瑞,吴乐南.基于正交分量辨别分析的人脸识别方法.模式识别与人工智能, 2001, 14(3): 372-375) [4] Choi E, Lee C. Optimizing Feature Extraction for Multiclass Problems. IEEE Trans on Geoscience and Remote Sensing, 2001, 39(3): 521-528 [5] Chen Zhijie. Advanced Algebra and Analytic Geometry. Beijing, China: Higher Education Press, 2000 (in Chinese) (陈志杰.高等代数与解析几何.北京:高等教育出版社, 2000) [6] Li Yuzhong, Liu Hongxing, Zhang Sheng. Improving Monkey-King Genetic Algorithm. Journal of Nanjing Normal University: Engineering and Technology, 2004, 4(3): 53-56 (in Chinese) (李宇中,刘红星,张 胜.猴王遗传算法的改进.南京师范大学学报:工程技术版, 2004, 4(3): 53-56)
2007, Vol. 20 
