F-norm distance metric based robust 2DPCA and face recognition

Two-dimensional principal component analysis (2DPCA) employs squared F-norm as the distance metric for dimensionality reduction. It is commonly known that squared F-norm is sensitive to the presence of outliers. To address this problem, we use F-norm instead of squared F-norm as the distance metric in the objective function and develop a non-greedy algorithm, which has a closed-form solution in each iteration and can maximize the criterion function, to solve the optimal solution. Our approach not only is robust to outliers but also well characterizes the geometric structure of data. Experimental results on several face databases illustrate that our method is more effective and robust than the other robust 2DPCA algorithms.