block principal component analysis with l(1)-norm (bpca-l1) has demonstrated its effectiveness in a lot of visual classification and data mining tasks. however, the greedy strategy for solving the l(1)-norm maximization problem is prone to being struck in local solutions. in this paper, we propose a bpca with nongreedy l(1)-norm maximization, which obtains better solutions than bpca-l1 with all the projection directions optimized simultaneously. other than bpca-l1, the new algorithm has been evaluated against some popular principal component analysis (pca) algorithms including pca-l1 and 2-d pca-l1 on a variety of benchmark data sets. the results demonstrate the effectiveness of the proposed method.