Dimensionality reduction plays a crucial role in machine learning and pattern recognition. The existing projection-based methods tend to solely utilize distance information or representation relationships among data points to maintain the data structure, which makes it difficult to effectively capture the nonlinear features and complex correlations of data manifolds in high-dimensional space. To address this issue, this study proposes a method: enhanced locality preserving projection with latent sparse representation learning (LPP_SRL). The method not only utilizes distance information to preserve the local structure of the data but also leverages multiple local linear representations to unveil the global nonlinear structure of the data. Moreover, to establish a connection between projection learning and sparse self-representation, this study employs a novel strategy by replacing the dictionary in sparse self-representation with reconstructed samples from the low-dimensional representation. This approach effectively filters out irrelevant features and noise, thereby better preserving the principal components in the original feature space. Extensive experiments conducted on multiple publicly available benchmark datasets have demonstrated the effectiveness and superiority of the proposed method.