Application of Logistic Regression with Fixed Memory Step Gradient Descent Method in Multi-Class Classification Problem
Yuan Sun, Zhihao Zhang, Zan Yang, Dan Li · 2019
Logistic regression is a supervised binary classification algorithm in machine learning. It is an important part of neural network and convolutional neural network. An important part of the logistic regression algorithm is to find the optimal parameters of the loss function, which is often a non-linear convex optimization problem. Generally, the gradient descent method or the stochastic gradient descent method in linear search are used in this nonlinear problem, but these methods are easy to fall into the trap of local minimum and have weak global convergence. In order to preserve the gradient information better and enhance the global convergence of the algorithm, this paper introduces fixed memory step gradient descent method into the optimization part of logistic regression algorithm, and combines OVR strategy to solve the problem of multi-class classification of data.