Principal Component Analysis Method with Gauss Quantum Particle Swarm Optimization

Principal component analysis is an unsupervised machine learning algorithm for linear dimensionality reduction of data. The essence of principal component analysis is to solve the problem of maximum variance of projection point. Generally, the gradient rise method is used to solve the optimal parameters of the objective function, but it is easy to fall into the local maximum trap. In this paper, the principal component analysis method with Gaussian quantum particle swarm is proposed to overcome the shortcomings of gradient ascent method, which makes the algorithm independent of gradient and has stronger global optimization performance.