Nuclear Norm Based 2DPCA
Fanlong Zhang, Jianjun Qian, Jian Jun Yang · 2013
This paper presents a novel method, namely nuclear norm based 2DPCA (N-2DPCA), for image feature extraction. Unlike the conventional 2DPCA, N-2DPCA uses a nuclear norm based reconstruction error criterion. The criterion is minimized by converting the nuclear norm based optimization problem into a series of F-norm based optimization problems. N-2DPCA is applied to face recognition and is evaluated using the Extended Yale B and CMU PIE databases. Experimental results demonstrate that our method is more effective and robust than PCA, 2DPCA and L1-Norm based 2DPCA.