Supervised Data-Dependent Kernel Low-Rank Preserving Projection for Image Recognition

Dimensionality reduction (DR) is a frequently used method to handle data with many dimensions. This article presents a novel approach to nonlinear dimensionality reduction, referred to as supervised data-dependent kernel low-rank preserving projection (SDKLRPP). Designed for image recognition tasks. The method enhances performance by utilizing a data-dependent kernel (DK). Unlike traditional nonlinear dimensionality reduction techniques, the SDKLRPP method described in this paper can fine-tune the coefficients within the DK and uncover The paper elucidates the low-rank reconstructive relationship inherent in the data. It also illustrates how the manifold structure within the feature space is mirrored in the label space, grounded in the principle of smoothness. This enables label information to direct the optimization of coefficients within the DK, thereby enhancing the capacity to discern among various classes. Upon attaining the optimal low-rank reconstructive relationship, a transformation matrix is computed to maintain this relationship, which is subsequently utilized to project the data into a reduced-dimensional space. The efficacy of the proposed SDKLRPP methodology is substantiated through experiments conducted on five extensively acknowledged databases.