中文摘要：

针对典型相关分析用于图像特征融合时，不仅消耗大量时间，且常常产生协方差阵奇异的问题，提出了一种快速算法．该算法将图像看作张量空间R^M×R^N中的二阶张量，建立方差和协方差，根据准则函数进行相关投影分析，将图像矩阵投影到2个向量空间的张量积空间．图像识别实验结果表明，该算法不仅提高了计算效率，而且能取得更高的识别率．

英文摘要：

In order to decrease the computational time and avoid the singularity of covariance matrix when canonical correlation analysis is used for feature fusion of images, a fast algorithm was proposed. The algorithm considers an image as the second order tensor in R^M×R^N. It is based on two-dimensional image matrices rather than vectors. So, variance and covariance can be constructed using the image matrices by building corresponding criterion function. After getting the projection matrices, it can project the image matrices onto a space which is the tensor product of two vector spaces. The relationship between the row vectors of the image matrix and that between column vectors can be naturally characterized by the proposed algorithm. The experiments suggest that the proposed algorithm can not only improve the computational efficiency greatly but also achieve much higher recognition accuracies.