Kernel sparsity preserving projections and its application to gait recognition
核稀疏保留投影及在步态识别中的应用

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.