针对已有基于线性变换的稀疏保留投影方法在解决实际问题时,会遇到维数灾难和小样本问题。通过引入核方法,提出一种核稀疏保留投影方法。首先采用非线性变换将原始数据映射到高维特征空间,而后在这个高维空间进行稀疏重构,并对得到的系数矩阵进行降维优化,最终得到所需的投影矩阵。将其应用到步态识别中,采用CASIA(B)步态数据库进行实验分析,实验结果表明,本文方法取得了令人满意的识别效果。

In order to solve the problem of the curse of dimensionality and the small sample problem, a kernel sparsity preserving projection is proposed. First, the nonlinear transformation is used to map the original data to a high-dimensional feature space. Then, the sparsity reconstruction in a high-dimensional space is used and, the coefficient matrix is reduced and optimized. Finally, the projection matrix is obtained. This method is evaluated on the CASIA (B) Gait database. The experimental results show that the proposed method can obtain stable classification and performs satisfactory recognition results.