Multi-Class Logistic Regression by Randomization and Nonlinearization

Abstract

In data science, system modeling is required to handle huge amounts and types of data. In this paper, we apply a new feature space generation method to multi-class logistic regression. We consider the improvement in accuracy and changes in characteristics of the feature space. The improvement is due to the synergistic effect of high dimensionality caused by randomization and nonlinearization. We verify that this method has desirable properties due to the matrix rank and eigenvalue distribution by using specific numerical examples.