基于全局约束的监督稀疏保持投影降维方法研究

Supervised Sparsity Preserving Projection Based on Global Constraint

非约束环境下采集的人脸图像复杂多变,稀疏保持投影降维效果不理想。鉴于此,提出一种基于全局约束的监督稀疏保持投影(SSPP-GC)算法。通过引入监督超完备字典和类内紧凑度约束,增强同类非近邻样本的重构关系;并且,在低维投影时增加全局约束因子,使得投影矩阵既考虑了样本的局部稀疏关系,也考虑了全局分布特性,进一步消除异类伪近邻样本的低维映射影响。在AR库、Extended Yale B库、LFW库和PubFig库上进行实验仿真,大量实验结果验证了本文算法的有效性。

The unconstrained face images collected in the real environments are influenced by many complicated and changeable interference factors,and sparsity preserving projection cannot well characterize the low-dimensional discriminant structure embedded in the high-dimensional unconstrained face images,which is important for the subsequent recognition task.To solve this problem,we propose an effective dimensionality reduction method named as supervised sparsity preserving projections based on global constraint（SSPP-GC）which firstly enhances the reconstruction relationship of the same class of samples by adopting supervised over-complete dictionary and coefficient compactness constraints,and then appends the global constraint penalty in the step of the lowdimensional projection to further weaken the influence of other classes of samples.The experimental results on AR,Extended Yale B,LFW and PubFig databases demonstrate the effectiveness of the proposed approach.