IEEE Trans Cybern. 2016 Nov;46(11):2543-2547. doi: 10.1109/TCYB.2015.2479645. Epub 2015 Oct 13.
Block principal component analysis with l -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 -norm maximization problem is prone to being struck in local solutions. In this paper, we propose a BPCA with nongreedy l -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.
基于 l-范数（BPCA-L1）的块主成分分析在许多视觉分类和数据挖掘任务中已经证明了其有效性。然而，求解 l-范数最大化问题的贪婪策略容易陷入局部解。在本文中，我们提出了一种非贪婪 l-范数最大化的 BPCA，它可以获得比同时优化所有投影方向的 BPCA-L1 更好的解。除了 BPCA-L1 之外，我们还在各种基准数据集上对新算法与一些流行的主成分分析（PCA）算法，包括 PCA-L1 和 2-D PCA-L1，进行了评估。结果证明了所提出方法的有效性。
