DUAL-SPARSITY PRESERVING PROJECTION

双重稀疏保持投影(英文)

Sparsity preserving projection（SPP） is a popular graph-based dimensionality reduction（DR） method, which has been successfully applied to solve face recognition recently. SPP contains natural discriminating information by preserving sparse reconstruction relationship of data sets. However, SPP suffers from the fact that every new feature learned from data sets is linear combinations of all the original features, which often makes it difficult to interpret the results. To address this issue, a novel DR method called dual-sparsity preserving projection （DSPP） is proposed to further impose sparsity constraints on the projection directions of SPP. Specifically, the proposed method casts the projection function learning of SPP into a regression-type optimization problem, and then the sparse projections can be efficiently computed by the related lasso algorithm. Experimental results from face databases demonstrate the effectiveness of the proposed algorithm.

稀疏保持投影(Sparsity preserving projection,SPP)是一种新型的基于图的降维方法,近年来被成功应用于人脸识别。SPP基于数据的稀疏重建关系建图,从而包含自然的判别信息。然而,经SPP变换后,新的特征是所有原始特征的线性组合,因此很难解释其降维结果。为此,提出了一种新的降维方法——双重稀疏保持投影(Dual-sparsity preserving projection,DSPP),通过进一步对SPP的投影方向施加稀疏约束,希望获得投影方向的稀疏解。具体地,该方法把SPP中投影函数的计算转化为一个回归类优化问题,然后借助L1正则化回归技术获得稀疏投影向量。在人脸数据上的实验结果表明了该算法的有效性。