A feature selection method based on Lorentzian metric

Nowadays multidimensional data as a part of Big Data are collected in every organization and feature selection is one of the main approaches in terms of processing them with machine learning methods. In this study, firstly, a Feature Selection based on Lorentzian Metric (FSLM) is developed. The proposed method unlike from matrix multiplication in Euclidean space uses the Lorentzian analogue to calculate within and between class scatter matrices. These scatter matrices are functioned in the novel FSLM method as discriminative criterion. In calculation of the scatter matrix, covariance matrix in Lorentzian space should be properly defined. The validity and correctness of the novel FSLM method was tested over GESTURE, LSVT, MADELON, RELAX and SONAR data sets. The obtained results indicate that the proposed method has a significant improvement in terms of feature selection and classification success.