Locally Linear Embedding Based on Gannet Optimization Algorithm

The analysis and feature extraction of high-dimensional data can help people solve various problems in current industry applications, and manifold learning is a type of data dimensionality reduction method developed based on the concept of topological manifolds. Locally linear embedding (LLE) algorithm, as a classic manifold learning algorithm, has attracted much attention due to its low computational complexity and strong scalability. However, the objective function of LLE algorithm may contain absolute value terms and cannot be guaranteed to be convex. Traditional derivative dependent solving methods are difficult to obtain its optimal solution, making it impossible to achieve effective feature extraction of data. In this paper, an LLE based on gannet optimization algorithm (GOA) has been proposed, and it is supposed to be used as a derivative-free optimization method to effectively solve the problems of non-differentiable of non-convex objective function, premature convergence, and easy to fall into local optimum in the process of solving the objective function of LLE.