Data dimension reduction method based on maximizing ratio sum for linear discriminant analysis

Abstract

This invention relates to a data dimension reduction method based on maximizing a ratio sum for linear discriminant analysis, which belongs to the fields of image classification and pattern recognition. It includes constructing a data matrix, a label vector and a label matrix; calculating a within-class covariance matrix and a between-class covariance matrix; constructing the optimization problem based on maximizing the ratio sum for the linear discriminant analysis; using the alternating direction method of multipliers to obtain the projection matrix which can maximize an objective function. This invention establishes the objective function based on maximizing the ratio sum for the linear discriminant analysis to avoid the problem that the traditional linear discriminant analysis tends to select features with small variances and weak discriminating ability. It can select features which are more conducive to classification. Moreover, this method does not depend on the calculation of the inverse matrix of the within-class covariance matrix and does not require data preprocessing, which improves the adaptability of the data dimensionality reduction method to the original data feature.