Index t-SNE Based on Sobol Sequence Initialized Harris Hawk Optimization Algorithm

It is of no doubt that artificial intelligence (AI) is a cutting-edge technological science, in which manifold learning methods have become a hot research topic in recent years. t-distribution stochastic neighbor embedding (t-SNE) is a representative nonlinear dimensionality reduction method in manifold learning. The 2 main advantages of t-SNE are its ability to maintain structural characteristics and solve congestion problems. However, t-SNE is not parametric, so the 2 initializations of the algorithm will result in 2 different embeddings, thus, a guiding index is added to t-SNE, and thus index t-SNE algorithm has been formed. Nevertheless, like traditional t-SNE, the optimization method of index t-SNE is still prone to fall into the trap of local minimum, resulting in no global optimal solution. And the closer to the global optimal solution, the more oscillatory the parameter optimization path will be, which means that the optimization efficiency of index t-SNE is not high. The root of causing the above problems is that the optimization method of index t-SNE relies on gradient or derivative information. In this paper, a derivative-free swarm intelligence method - Sobol sequence initialized Harris hawk optimization (HHO) has been utilized to overcome the above shortcomings, and the advantages of the proposed method have been proved via numerical experiments.