摘要
不相关空间算法是一种基于 Fisher 准则求解不相关鉴别矢量集的快速算法,但应用在人脸识别中将遇到小样本问题.本文提出一种改进的不相关空间算法,较有效地解决这一问题.其思想是将原始数据空间降到一个低维的子空间,从而避免了总体散布矩阵奇异,并在理论上证明,在这个子空间中求解不相关鉴别矢量集等价于在原空间中求解不相关鉴别矢量集.另外根据散布矩阵的对称性,引入一种计算方法,进一步提高求解不相关鉴别矢量集的速度.最后,在人脸库上的实验结果验证该算法的有效性.
Uncorrelated space algorithm based on the fisher criterion function is a fast method for extracting uncorrelated discriminant vectors, but it may have the small size sample problem when applied in face recognition. And thus an improved uncorrelated space algorithm is proposed. It effectively overcomes the small size sample problem. The main idea of the proposed algorithm is to map the original space into a low dimensional subspace, and then the singularity of the total-scatter matrix can be avoided in this low dimensional subspace. It is proved that the uncorrelated discriminant vectors derived in this low dimensional subspace are equal to those derived in the original space. In addition, according to the symmetry of scatter matrix, a fast method is introduced to further speed up the computation of uncorrelated discriminant vectors. Finally, the experimental results on facial databases demonstrate the effectiveness of the proposed algorithm.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2008年第5期615-620,共6页
Pattern Recognition and Artificial Intelligence
基金
中国博士后基金(No.20060400809)
黑龙江省青年科技基金(No.QC06C022)资助项目
关键词
不相关空间算法
不相关鉴别矢量集
小样本问题
总体散布矩阵
Uncorrelated Space Algorithm, Uncon＇elated Discriminant Vectors, Small Size Sample Problem, Total-Scatter Matrix
