Angle 2DPCA: A New Formulation for 2DPCA
针对传统2DPCA对异常值敏感的问题，提出Angle-2DPCA方法，采用L1范数作为距离度量，并考虑重构误差与方差的关系，通过快速迭代算法求解，在人脸数据库上验证了有效性。
2-D principal component analysis (2DPCA), which employs squared -norm as the distance metric, has been widely used in dimensionality reduction for data representation and classification. It, however, is commonly known that squared -norm is very sensitivity to outliers. To handle this problem, we present a novel formulation for 2DPCA, namely Angle-2DPCA. It employs -norm as the distance metric and takes into consideration the relationship between reconstruction error and variance in the objective function. We present a fast iterative algorithm to solve the solution of Angle-2DPCA. Experimental results on the Extended Yale B, AR, and PIE face image databases illustrate the effectiveness of our proposed approach.