Matrix Variate Logistic Regression Model with Application to EEG Data

TL;DR

This paper introduces a matrix variate logistic regression model that preserves the structure of matrix covariates, improving efficiency and accuracy in biomedical applications like EEG data classification.

Contribution

The paper proposes the MV-logistic regression model, a novel approach that maintains matrix structure and reduces parameters compared to traditional methods.

Findings

Achieved high classification accuracy on EEG data

Successfully extracted structural effects of covariates

Demonstrated advantages over conventional logistic regression

Abstract

Logistic regression has been widely applied in the field of biomedical research for a long time. In some applications, covariates of interest have a natural structure, such as being a matrix, at the time of collection. The rows and columns of the covariate matrix then have certain physical meanings, and they must contain useful information regarding the response. If we simply stack the covariate matrix as a vector and fit the conventional logistic regression model, relevant information can be lost, and the problem of inefficiency will arise. Motivated from these reasons, we propose in this paper the matrix variate logistic (MV-logistic) regression model. Advantages of MV-logistic regression model include the preservation of the inherent matrix structure of covariates and the parsimony of parameters needed. In the EEG Database Data Set, we successfully extract the structural effects of covariate matrix, and a high classification accuracy is achieved.