T-SNE Dimensionality Reduction Method Based on Sobol Sequence Initialized Archerfish Hunting Optimizer Algorithm

Abstract

t-distributed stochastic neighbor embedding (t-SNE), is a famous supervised, nonlinear dimensionality reduction and data visualization method in manifold learning, its core idea is to pursue the probability isomorphism of data points from high-dimensional space to low-dimensional space, that is, it requires the points in high-dimensional space to meet a certain probability distribution, and still meet a similar probability distribution after projecting them into low-dimensional space. Generally, t-SNE algorithm will be eventually transformed into a problem of solving Kullback-Leibler (KL) divergence by gradient descent (GD) method or stochastic gradient descent (SGD) method. However, gradient dependent methods are easy to fall into the trap of local optimum, and the closer they are to the optimal value, the more oscillatory sawtooth effect will occur. Therefore, in order to overcome the shortcomings, in this paper, a novel Sobol sequence initialized archerfish hunting optimizer (SSAHO) has been proposed, which can increase the randomness and robustness of the algorithm, and via numerical experiment, its performance in improving the optimization ability has been verified.