Application of Principal Component Analysis with High-Order Truncated Gradient Ascent Method in Data Dimension Reduction

摘要

Principal component analysis is an unsupervised linear dimension reduction method in machine learning. The basic idea of principal component analysis is to project the data of feature space into low dimensional space, and ensure the maximum variance of projection point, which turns the problem into the optimal parameter problem that can make the maximum variance. In the field of machine learning, the gradient ascent method is usually used to solve the optimal parameters, but it is easy to fall into the local maximum trap. In this paper, a principal component analysis method with high- order truncated gradient ascent method is proposed. High- order truncated gradient ascent method is used to replace the gradient ascent method so as to better retain the gradient information and have better global convergence.